CSE

Program Educational Objectives (PEO): Within 3-5 years of graduation, the graduates of CSE will be able to:

PEO-1: Think Critically: Use problem-solving, decision-making and research skills to identify and solve complex problems needed to pursue a diverse range of professions.

PEO-2: Implementation Efficiency: Develop and implement efficient, sustainable, scalable, manageable, and future-proof solutions to problems through continuous learning.

PEO-3: Society, Ethics and Team Player: ethically manage independent or team work considering the societal, health and safety, and environmental impact.

PEO-4: Communication: Graduates will be able to disseminate information clearly and precisely to a broad range of audiences.

Mapping between Mission and PEOs:

Missions	PEO-1	PEO-2	PEO-3	PEO-4
Mission 1:	√	√
Mission 2:	√	√
Mission 3:	√	√	√
Mission 4:			√	√
Mission 5:	√	√	√	√
Mission 6:	√	√	√	√
Mission 7:	√	√	√	√

PROGRAM OUTCOMES (POs):

The BS-CSE has twelve program outcomes adopted from the preferred outcomes of both Computing Accreditation Commission (CAC) and Engineering Accreditation Commission (ECA) of ABET. The students who complete the BSc-CSE program will have:

PO1: Engineering knowledge: Apply knowledge of mathematics, natural science, engineering fundamentals and an engineering specialization as specified in K1 to K4 respectively to the solution of complex engineering problems.

PO2: Problem analysis: Identify, formulate, research literature and analyse complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences and engineering sciences. (K1 to K4)

PO3: Design/development of solutions: Design solutions for complex engineering problems and design systems, components or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations. (K5)

PO4: Conduct investigations of complex problems: Conduct investigations of complex problems using research-based knowledge (K8) and research methods including design of experiments, analysis and interpretation of data, and synthesis of information to provide valid conclusions.

PO5: Modern tool usage: Create, select and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modelling, to complex engineering problems, with an understanding of the limitations. (K6)

PO6: The engineer and society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to professional engineering practice and solutions to complex engineering problems. (K7)

PO7: Environment and sustainability: Understand and evaluate the sustainability and impact of professional engineering work in the solution of complex engineering problems in societal and environmental contexts. (K7)

PO8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of engineering practice. (K7)

PO9: Individual work and teamwork: Function effectively as an individual, and as a member or leader in diverse teams and in multi-disciplinary settings.

PO10: Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

PO11: Project management and finance: Demonstrate knowledge and understanding of engineering management principles and economic decision-making and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.

PO12: Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

PEO and PO Mapping:

PO No.	PO Statement	PEO-1	PEO-2	PEO-3	PEO-4
PO No.	PO Statement	Thinking Critically	Implement Efficiently	Society, Ethics, and Team Player	Communication
PO1	Engineering Knowledge	√
PO2	Problem Analysis	√
PO3	Design/ Development of Solutions		√	√	√
PO4	Conduct investigations of complex problems	√
PO5	Modern Tool Usage		√	√
PO6	The Engineer and Society			√
PO7	Environment and Sustainability		√	√
PO8	Ethics			√
PO9	Individual and Team Work			√	√
PO10	Communication			√	√
PO11	Project Management and Finance		√
PO12	Life-long Learning		√

Knowledge Profile

The knowledge profile should have eight attributes (K1 to K8), indicating the volume of learning and attributes against which graduates must be able to perform.

K1: A systematic, theory-based understanding of the natural sciences applicable to the discipline
K2: Conceptually-based mathematics, numerical analysis, statistics and formal aspects of computer and information science to support analysis and modelling applicable to the discipline
K3: A systematic, theory-based formulation of engineering fundamentals required in the engineering discipline
K4: Engineering specialist knowledge that provides theoretical frameworks and bodies of knowledge for the accepted practice areas in the engineering discipline; much is at the forefront of the discipline.
K5: Knowledge that supports engineering design in a practice area
K6: Knowledge of engineering practice (technology) in the practice areas in the engineering discipline
K7: Comprehension of the role of engineering in society and identified issues in engineering practice in the discipline: ethics and the professional responsibility of an engineer to public safety; the impacts of engineering activity: economic, social, cultural, environmental and sustainability
K8: Engagement with selected knowledge in the research literature of the discipline

A program that builds this type of knowledge and develops the attributes listed above is typically achieved in 4 to 5 years of study, depending on the level of students at entry.

Complex Engineering Problems

Complex engineering problems are those that include a wide range of or conflicting technical, engineering, and other challenges, have no clear solution, and necessitate analytical thinking and originality in analysis to design effective models. The ability to solve complicated problems in engineering is vital in the engineering curriculum. The list of complex engineering problems (P1 to P7) clarifies the concept of Complex Engineering Problem by establishing seven problem-solving ranges or features.

P1- (Depth of knowledge required) Cannot be resolved without in-depth engineering knowledge at the level of one or more of K3, K4, K5, K6 or K8 which allows a fundamentals-based, first principles analytical approach.
P2- (Range of conflicting requirements) Involve wide-ranging or conflicting technical, engineering and other issues.
P3- (Depth of analysis required) Have no obvious solution and require abstract thinking, originality in analysis to formulate suitable models.
P4- (Familiarity of issues) Involve infrequently encountered issues
P5- (Extent of applicable codes) Are outside problems encompassed by standards and codes of practice for professional engineering.
P6- (Extent of stakeholder involvement and conflicting requirements) Involve diverse groups of stakeholders with widely varying needs.
P7- (Interdependence) Are high level problems including many component parts or sub-problems.

Complex Engineering Activities

There are five attributes of activities students can be involved in when solving Complex Engineering Problem. A Complex Engineering Activity or Project is that which has some or all of the following attributes:

A1- (Range of resources) Involve the use of diverse resources (and for this purpose resources include people, money, equipment, materials, information and technologies).
A2- (Level of interaction) Require resolution of significant problems arising from interactions between wide-ranging or conflicting technical, engineering or other issues.
A3- (Innovation) Involve creative use of engineering principles and research-based knowledge in novel ways.
A4- (Consequences for society and the environment) Have significant consequences in a range of contexts, characterized by difficulty of prediction and mitigation.
A5- (Familiarity) Can extend beyond previous experiences by applying principles-based approaches

Bloom's Taxonomy

Level	Cognitive (C)	Affective (A)	Psychomotor (P)
1	Remember	Receive	Imitation
2	Understand	Respond	Manipulation
3	Apply	Value	Precision
4	Analyze	Organization	Articulation
5	Evaluate	Characterization	Naturalization
6	Create	-	-

Course List (Group Wise)

Language and General Education Courses

Basic Science (6 credits)

SL.

Course Code

Course Title

Credit

Hour

1

PHY 101

Engineering Physics I

3

2

PHY 103

Engineering Physics II

3

Language (7 credits)

SL.	Course Code	Course Title	Credit Hour
1	ENG 101	General English	3
2	ENG 103	Developing English Skills	2
3	ENG 401	Technical Writing & Presentation	2

Mathematics (12 credits)

SL.	Course Code	Course Title	Credit Hour
1	MAT 105	Engineering Mathematics I	3
2	MAT 107	Engineering Mathematics II	3
3	MAT 201	Engineering Mathematics III	3
4	MAT 203	Engineering Mathematics IV	3

Core (107 credits)

SL.	Course Code	Course Title	Credit Hour
1	CSE 110	Introduction to Computer Systems (Laboratory)	2
2	EEE 101	Electrical Circuits I	3
3	EEE 102	Electrical Circuits I Laboratory	1.5
4	ME 102	Mechanical Engineering Drawing & CAD (Laboratory)	1
5	CSE 103	Discrete Mathematics	3
6	CSE 111	Structured Programming	2
7	CSE 112	Structured Programming Laboratory	2
8	EEE 211	Electronics I	3
9	EEE 212	Electronics I Laboratory	1.5
10	CSE 211	Object Oriented Programming	3
11	CSE 212	Object Oriented Programming Laboratory	1.5
12	CSE 221	Data Structures	3
13	CSE 222	Data Structures Laboratory	1.5
14	EEE 311	Digital Electronics	3
15	EEE 312	Digital Electronics Laboratory	1.5
16	CSE 225	Algorithm Design and Analysis	3
17	CSE 226	Algorithm Design and Analysis Laboratory	1
18	CSE 237	Database Management Systems	3
19	CSE 238	Database Management Systems Laboratory	1.5
20	EEE 201	Signals & Systems	3
21	EEE 202	Signals & Systems Laboratory	1
22	CSE 301	Computational Methods for Engineering Problems	3
23	CSE 302	Computational Methods for Engineering Problems Laboratory	1
24	CSE 305	Software Engineering & Information System Design	4
25	CSE 306	Software Engineering & Information System Design Laboratory	1.5
26	EEE 309	Communication Engineering	3
27	EEE 310	Communication Engineering Laboratory	1.5
28	EEE 371	Microprocessors & Microcontrollers	3
29	EEE 372	Microprocessors & Microcontrollers Laboratory	1.5
30	CSE 317	Artificial Intelligence	3
31	CSE 318	Artificial Intelligence Laboratory	1.5
32	CSE 333	Operating Systems	3
33	CSE 334	Operating Systems Laboratory	1.5
34	CSE 337	Computer Organization & Architecture	3
35	CSE 338	Software Development Project (Laboratory)	2
36	CSE 363	Data Communication	3
37	CSE 367	Computer Networks	3
38	CSE 368	Computer Networks Laboratory	1.5
39	EEE 313	Control Systems	3
40	EEE 314	Control Systems Laboratory	1.5
41	CSE 309	Theory of Computation	2
42	CSE 451	Neural Network & Fuzzy Logic	3
43	CSE 452	Neural Network & Fuzzy Logic Laboratory	1
44	CSE 437	Network and Computer Security	3
45	CSE 453	Compiler Construction	3
46	CSE 454	Compiler Construction Laboratory	1.5
47	CSE 400	Project/Thesis	4

Non-Engineering (12 credits)

SL.	Course Code	Course Title	Credit Hour
1	ACC 101	Basic Accounting	3
2	ECO 201	Basic Economics	3
3	MGT 203	Industrial and Business Management	3
4	MGT 251	Organizational Behavior	3

Electives (16 credits)

SL.	Course Code	Course Title	Credit Hour
1	CSE 455	Computer Graphics & Image Processing (Elective)	3
2	CSE 456	Computer Graphics & Image Processing Laboratory (Elective)	1
3	CSE 457	Machine Learning (Elective)	3
4	CSE 458	Machine Learning Laboratory (Elective)	1
5	CSE 455	Complementary Computer Course (Elective)	3
6	CSE 456	Complementary Computer Course (Elective)	1
7	CSE 459	Pattern Recognition (Elective)	3
8	CSE 460	Pattern Recognition Laboratory (Elective)	1

Course List (Semester Wise)

Distribution of Courses

Term-wise course outline for the entire program:

Level one Term one
Course No.	Course Title	Credit Hours	Contact Hours
ACC 101	Basic Accounting	3	3
CSE 110	Introduction to Computer Systems (Laboratory)	2	4
EEE 101	Electrical Circuits I	3	3
EEE 102	Electrical Circuits I Laboratory	1.5	3
ENG 101	General English	3	3
MAT 105	Engineering Mathematics I	3	3
ME 102	Mechanical Engineering Drawing & CAD (Laboratory)	1	2
PHY 101	Engineering Physics I	3	3
Total	Theory: 5 Sessional: 3	19.5	24

Level one Term two
Course No.	Course Title	Credit Hours	Contact Hours
CSE 103	Discrete Mathematics	3	3
CSE 111	Structured Programming	2	2
CSE 112	Structured Programming Laboratory	2	4
EEE 211	Electronics I	3	3
EEE 212	Electronics I Laboratory	1.5	3
ENG 103	Developing English Skills	2	2
MAT 107	Engineering Mathematics II	3	3
PHY 103	Engineering Physics II	3	3
Total	Theory: 6 Sessional: 2	19.5	23

Level two Term one
Course No.	Course Title	Credit Hours	Contact Hours
CSE 211	Object Oriented Programming	3	3
CSE 212	Object Oriented Programming Laboratory	1.5	3
CSE 221	Data Structures	3	3
CSE 222	Data Structures Laboratory	1.5	3
ECO 201	Basic Economics	3	3
EEE 311	Digital Electronics	3	3
EEE 312	Digital Electronics Laboratory	1.5	3
MAT 201	Engineering Mathematics III	3	3

Total	Theory: 5 Sessional: 3	19.5	24

Level two Term two
Course No.	Course Title	Credit Hours	Contact Hours
CSE 225	Algorithm Design and Analysis	3	3
CSE 226	Algorithm Design and Analysis Laboratory	1	2
CSE 237	Database Management Systems	3	3
CSE 238	Database Management Systems Laboratory	1.5	3
EEE 201	Signals & Systems	3	3
EEE 202	Signals & Systems Laboratory	1	2
MAT 203	Engineering Mathematics IV	3	3
MGT 203	Industrial and Business Management	3	3
Total	Theory: 5 Sessional: 3	18.5	22

Level three Term one
Course No.	Course Title	Credit Hours	Contact Hours
CSE 301	Computational Methods for Engineering Problems	3	3
CSE 302	Computational Methods for Engineering Problems Laboratory	1	2
CSE 305	Software Engineering & Information System Design	4	4
CSE 306	Software Engineering & Information System Design Laboratory	1.5	3
EEE 309	Communication Engineering	3	3
EEE 310	Communication Engineering Laboratory	1.5	3
EEE 371	Microprocessors & Microcontrollers	3	3
EEE 372	Microprocessors & Microcontrollers Laboratory	1.5	3
MGT 251	Organizational Behavior	3	3
Total	Theory: 5 Sessional: 4	21.5	27

Level three Term two
Course No.	Course Title	Credit Hours	Contact Hours
CSE 317	Artificial Intelligence	3	3
CSE 318	Artificial Intelligence Laboratory	1.5	3
CSE 333	Operating Systems	3	3
CSE 334	Operating Systems Laboratory	1.5	3
CSE 337	Computer Organization & Architecture	3	3
CSE 338	Software Development Project (Laboratory)	2	4
CSE 364	Data Communication	3	3
CSE 367	Computer Networks	3	3
CSE 368	Computer Networks Laboratory	1.5	3
Total	Theory: 5 Sessional: 4	21.5	26

Level four Term one
Course No.	Course Title	Credit Hours	Contact Hours
EEE 313	Control Systems	3	3
EEE 314	Control Systems Laboratory	1.5	3
ENG 401	Technical Writing & Presentation	2	2
CSE 309	Theory of Computation	2	2
CSE 451	Neural Network & Fuzzy Logic	3	3
CSE 452	Neural Network & Fuzzy Logic Laboratory	1	2
CSE 455	Computer Graphics & Image Processing (Elective)	3	3
CSE 456	Computer Graphics & Image Processing Laboratory (Elective)	1	2
CSE 437	Network and Computer Security	3	3
Total	Theory: 6, Sessional: 3	19.5	23

Level four Term two
Course No.	Course Title	Credit Hours	Contact Hours
CSE 453	Compiler Construction	3	3
CSE 454	Compiler Construction Laboratory	1.5	3
CSE 457	Machine Learning (Elective)	3	3
CSE 458	Machine Learning Laboratory (Elective)	1	2
CSE 481	Contemporary Courses of Computer Course (Elective)	3	3
CSE 482	Contemporary Courses of Computer Course Laboratory (Elective)	1	2
CSE 459	Pattern Recognition (Elective)	3	3
CSE 460	Pattern Recognition Laboratory (Elective)	1	2
CSE 400	Project/Thesis	4	4
Total	Theory: 4 Sessional: 5	20.5	25

Four Optional courses (Theory + Lab) have to be selected from the list given below:

CSE 411	Project Management for Information Systems	3
CSE 412	Project Management for Information Systems Laboratory	1
CSE 417	Advanced Software Engineering	3
CSE 418	Advanced Software Engineering Laboratory	1
CSE 451	Neural Network & Fuzzy Logic	3
CSE 452	Neural Network & Fuzzy Logic Laboratory	1
EEE 319	Telecommunication & Switching	3
EEE 320	Telecommunication & Switching Laboratory	1
EEE 403	Microwave & Antenna Engineering	3
EEE 404	Microwave & Antenna Engineering Laboratory	1
EEE 435	Cellular Mobile Communication	3
EEE 469	Optical Fiber Communication	3
EEE 470	Optical Fiber Communication Laboratory	1
CSE 431	Wireless Technologies	3
CSE 432	Wireless Technologies Laboratory	1
CSE 433	Advanced Switching & Routing Concepts	3
CSE 434	Advanced Switching & Routing Concepts Laboratory	1
CSE 435	WAN Technologies	3
CSE 436	WAN Technologies Laboratory	1
CSE 415	Human Computer Interaction	3
CSE 416	Human Computer Interaction Laboratory	1
CSE 419	Basic Multimedia Systems	3
CSE 420	Basic Multimedia Systems Laboratory	1
CSE 455	Computer Graphics & Image Processing	3
CSE 456	Computer Graphics & Image Processing Laboratory	1
CSE 457	Machine Learning	3
CSE 458	Machine Learning Laboratory	1
CSE 459	Pattern Recognition	3
CSE 460	Pattern Recognition Laboratory	1
CSE 461	Management Information System	3
CSE 462	Management Information System Laboratory	1
CSE 463	Graph Theory	3
CSE 464	Graph Theory Laboratory	1
CSE 465	Distributed AI and Intelligent Agent	3
CSE 466	Distributed AI and Intelligent Agent Laboratory	1
CSE 467	Parallel & Distributed Computing	3
CSE 468	Parallel & Distributed Computing Laboratory	1
CSE 471	VoIP	3
CSE 472	VoIP Laboratory	1
CSE 477	Computer Interfacing	3
CSE 478	Computer Interfacing Laboratory	1
CSE 481	Computational Geometry	3
CSE 482	Computational Geometry Laboratory	1
CSE 483	Digital System Design	3
CSE 484	Digital System Design Laboratory	1
EEE 443	VLSI Design	3
EEE 444	VLSI Design Laboratory	1
EEE 455	Satellite Communication	3
EEE 456	Satellite Communication Laboratory	1
EEE 477	Digital Signal Processing	3
EEE 478	Digital Signal Processing Laboratory	1
CSE 485	Contemporary course of Computer Science I	3
CSE 486	Contemporary course of Computer Science I Laboratory	1
CSE 487	Contemporary course of Computer Science II	3
CSE 488	Contemporary course of Computer Science II Laboratory	1

Dominant Courses for PO Attainment

Dominant Courses:

Discrete Mathematics (CSE 103)
Structured Programming (CSE 111)
Structured Programming Lab (CSE 112)
Algorithm Design and Analysis (CSE 225)
Algorithm Design and Analysis Lab (CSE 226)
Software Engineering and Information System Design (CSE 305)
Software Engineering and Information System Design Lab (CSE 306)
Microprocessor and Microcontroller (EEE 371)
Microprocessor and Microcontroller Lab (EEE 372)
Artificial Intelligence (CSE 317)
Artificial Intelligence Lab (CSE 318)
Software Development (CSE 338)
Computer Networks (CSE 367)
Computer Networks Lab (CSE 368)
Project/Thesis (CSE 400)

Course Code

Program Outcomes

Knowledge Profile

Complex Engineering Problem Solving

Complex Engineering Activities

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

K1

K2

K3

K4

K5

K6

K7

K8

EP1

EP2

EP3

EP4

EP5

EP6

EP7

EA1

EA2

EA3

EA4

EA5

K1-K4

K5

K8

K6

K7

Related to PO10

Complex Engineering Problem Solution

Engineering Knowledge

Problem Analysis

Design/development to solutions

Investigation

Modern tool usage

The engineer and society

Environment and sustainability

Ethics

Indivdual work and team work

Communication

Project management and finance

Life-long Learning

Science

Math

Engg. Fundamentals

Engg. Specialization

Design

Technology

Society

Research

Knowledge k3-k6,k8

Wide ranging/ Conflicting

Depth of analysis

Familiarity of issues

Extent of applicable codes

Extent of stakeholders

Interdependencce

Range of resources

Level of interaction

Innovation

Consequences

Familiarity

CSE 111 (SP)

√

CSE 112 (SPL)

√

CSE 103 (DM)

√

CSE 225 (ADA)

√

CSE 226 (ADAL)

√

CSE 305 (SEISD)

√

CSE 306 (SEISDL)

√

EEE 371 (MM)

√

EEE 372 (MML)

√

CSE 317 (AI)

√

CSE 318 (AIL)

√

CSE 338 (SD)

√

CSE 367 (CN)

√

CSE 368 (CNL)

√

CSE 400 (Thesis/ Project)

√

Course Content

Basic Science

Course Title: Engineering Physics I

Course Code: PHY 101

Credits: 3	Class Hours/Week: 3
Course Type: Basic Science	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is intended to build up strong base in mechanics, waves and oscillations, and thermal science as well as to include the experience of applications of its contents in the relevant courses of science/engineering.

Course Objectives:

The objectives of this course are:

To enhance the understanding and problem-solving skills on related/necessary basic topics in mechanics, waves and oscillations, and thermal science required for learning the related topics in the courses of undergraduate program in computer science and engineering.
To develop the basic concepts of mechanics, waves and oscillations, and thermal science in their relevant scope.
To builds up problem solving experience in each relevant topic along with addition of application familiarity in the related areas of science/engineering. Etc.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Identify (C1) typical simple and moderately complex problems of selected topics and of the linked topics of science/engineering courses of undergraduate programs;
CO2	Develop (C2) mathematical expressions of different physical laws of knowledge and skills acquired in the related more advanced courses.
CO3	Apply (C3) different physical laws for solving related unusual/advanced problems.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Mechanics: Kinematics, Graphical representations of displacement-time, velocity-time and acceleration-time.	CO1
2.	Motion in two and three dimensions-projectile motion, Application of Newton’s laws of motion; Equilibrium forces. Work-kinetic energy theorem. Power; Conservation of energy, Conservation of linear momentum for a system of particles. Center-of-mass motion. Elastic and inelastic collision in one dimension, Gravitation: Gravitational field, Kepler’s laws	CO1, CO2, CO3
3.	Waves & Oscillations: Simple Harmonic motion, damped simple harmonic oscillation, forced oscillations, Combination and Composition of simple harmonic motions, Lissajous figures, Transverse and Longitudinal nature of waves, traveling and standing waves, intensity of waves, Energy calculation of progressive & stationary waves, Phase velocity, group velocity, Velocity of Longitudinal wave in a gaseous medium, Doppler effect.	CO1, CO2, CO3
4.	Thermodynamics: Heat and temperature. Zeroth and 1st law of thermodynamics, Isothermal and adiabatic relations, Second law of thermodynamics. Reversible and irreversible processes, Carnot cycle, Auto cycle, Diesel cycle and their efficiency, Clausius theorem. Entropy. Absolute scale of temperature. Clausius Clapeyron equation. Thermodynamic functions, Maxwell’s thermodynamic relations. Problem involving thermodynamic relations and functions. Gibb’s phase rule	CO1, CO2, CO3

Text Books, Reference Books and Other Resources:

An Introduction to Mechanics by Daniel Kleppner, Robert Kolenkow
A text book of Sound by Brij Lal and N. Subrahmanyam
A Textbook of Heat by M. N. Saha and B. N. Srivastava
Heat and Thermodynamics by Brij Lal and N. Subrahmanyam
Physics for Engineers Part 1 by Dr. Giasuddin Ahmed
Physics Vol. 1 by Robert Resnick, David Halliday, Kenneth S. Krane
Heat and Thermodynamics: An Intermediate Textbook by Richard H.Dittman and
Mark W. Zemansky
Acoustics & Vibrational Physics by Stephenson

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C1	Lecture notes, PPT slides, problem solving, web content	Assignment, Class Test, Midterm, Final exam
CO2	PO1	C2	Lecture notes, PPT slides, problem solving, web content	Assignment, Class Test, Midterm, Final exam
CO3	PO1	C3	Lecture notes, PPT slides, problem solving, web content	Assignment, Class Test, Midterm, Final exam

Course Title: Engineering Physics II

Course Code: PHY 103

Credits: 3	Class Hours/Week: 3
Course Type: Basic Science	Pre-requisite: EP I
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is intended to build up strong base in electromagnetism, optics, and modern physics, as well as to include the experience of applications of its contents in the relevant courses of science/engineering.

Course Objectives:

This course enhances the understanding and problem-solving skills on related/necessary basic topics in electromagnetism, optics and modern physics required for learning the related topics in the courses of undergraduate program in computer science and engineering.
Toward the above goal, it develops the basic concepts of electromagnetism, optics, and modern physics in their relevant scope.
While doing the above it builds up problem solving experience in each relevant topic along with addition of application familiarity in the related areas of science/engineering. Etc.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Identify (C1) typical simple and moderately complex problems of selected topics and of the linked topics of science/engineering courses of undergraduate programs;
CO2	Develop (C2) mathematical expressions of different physical laws of knowledge and skills acquired in the related more advanced courses.
CO3	Apply (C3) different physical laws for solving related unusual/advanced problems.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Electricity and Magnetism: Electromagnetism: Magnetic fields, Maxwell’s equations, Ampere’s law, Faraday’s law, Lenz’s law. Inductance: Self mutual inductance.	CO1, CO2, CO3
2.	Magnetic properties of matter: Magnetomotive force, magnetic field intensity, permeability and susceptibility	CO1, CO2, CO3
3.	classification of magnetic materials, magnetization curve of ferromagnetic materials, magnetic circuits, magnetostriction;	CO1, CO2, CO3
4.	Optics: Theories of light; Huygen’s principles and constructions;	CO1, CO2, CO3
5.	Interference of light: Young’s double slit experiment, Fresnel bi-prism, Newton’s ring, interferometers	CO1, CO2, CO3
6.	Diffraction of light: Diffraction, Fresnel Fraunhoffer diffraction, Diffraction by single slit, Diffraction by double slit,	CO1, CO2, CO3
7.	Polarization of light: Polarization of electromagnetic waves, production and analysis of polarized light, optical activity, Optics of crystals;	CO1, CO2, CO3
8.	Lasers and their Applications: Laser introduction, Stimulated and spontaneous radiation's coherence, Resonators Ruby, and other laser. Material processing with shaping	CO1, CO2, CO3
9.	Modern physics: Relativity, Michelson-Morley experiment, Lorenz-Einstein transformation, Mass energy relation, Quantum effect, Photoelectric effect, Compton Effect;	CO1, CO2, CO3
10.	Atomic Physics: De-Broglie wave, correspondence principles, uncertainty principle, The Rutherford-Bohr model of the atom, Energy levels and spectra,	CO1, CO2, CO3
11.	The Zeeman effect, Electron spin, Many electron atoms and the exclusion principle, vector atom model;	CO1, CO2, CO3
12.	Nuclear Physics: Introduction, Nuclear constituents, Nuclear binding and nuclear structure, Radioactivity, Radioactive decay, Half-life,	CO1, CO2, CO3
13.	Law of successive disintegration, Radioactive equilibrium, The nucleus, Properties of nucleus, Binding energy, Nuclear reactions, Nuclear fission and fusion, nuclear reactors.	CO1, CO2, CO3

Text Books, Reference Books and Other Resources:

Text books:

M. S. Huq, A. K. Rafiqullah, A. K. Roy Concept of Electricity & Magnetism
Dr. Giasuddin Ahmed Physics for Engineers part 2
Kailash K. Sharma Optics: Principles and Applications
S. N. Ghoshal Nuclear Physics

References:

K. K. Tewari Electricity and Magnetism with Electronics
D. N. Vasudeva Fundamentals of Magnetism and Electricity
Francis A. Jenkins and Harvey E. White Fundamentals of Optics
Floyd Karker Richtmyer, Earle Hesse Kennard, John N. Cooper Introduction to Modern Physics
Irving Kaplan Nuclear Physics

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C1	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam
CO2	PO1	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam
CO3	PO1	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam

Language

Course Title: General English

Course Code: ENG 101

Credits: 3	Class Hours/Week: 3
Course Type: Language	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course aims to introduce the basic grammar of English language.

Course Objectives:

The objectives of this course are:

To focus on the basics of English grammar for developing integrated language skills.
To develop the students’ habit of thinking logically and clearly.
To provide basic grammar rules and involve students into extensive practice.
To make learners competent in English by the accurate use of grammar.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Discuss (C2, A2) the properties of spoken English (speaking and listening skills) and written English (reading and writing skills).
CO2	Discuss (C2, A2) the factors how grammar and vocabulary choices together create a range of different meanings in speech and writing.
CO3	Outline (C3, A2) the different ways in which grammar has been described.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

C O1

C O2

C O3

Course Description:

SL No.	Course Content	COs
1.	Sounds and Pronunciation: English sounds and pronunciation development	CO1
2.	Grammar and Vocabulary: Content related words and expressions and how to apply them in written texts	CO2
3.	Tense and types, Parts of Speech: Noun, pronoun, adjective, adverb, verb, Preposition and article, Conditionals	CO3
4.	Phrasal Verb	CO2
5.	Wh/yes-no questions	CO3

Text Books : (for grammar purpose)

Author Book Name

Raymond Murphy Intermediate English Grammar
John Eastwood Oxford English Grammar
Cliff’s TOEFL - a handbook
P.K. Dey Sarkar A textbook Higher English Grammar
R. A. Close The English We Use
W. Stannard Allen Living English Structure

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO10	C2, A2	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO10	C2, A2	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO10	C3, A2	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final Exam

Course Title: Developing English Skills

Course Code: ENG 103

Credits: 2	Class Hours/Week: 2
Course Type: Language	Pre-requisite: GE
CIE Marks: 100%	SEE Marks:

Course Rationale:

This is an advanced course for students to learn reading, writing, listening and speaking skills.

Course Objectives:

The objectives of this course are to:

Develop a student's writing skill, listening skill.
Improve the ability to note taking.
Grow students speaking English through group discussion.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	D Demonstrate (C2) and practice (A2) English listening and speaking effectively.
CO2	Demonstrate (C2) and practice (A2) English writing and reading skills.
CO3	Communicate (C3) and work (A2) collaboratively with others.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

C O1

C O2

C O3

Course Description:

SL No.	Course Content	COs
1.	Developing Writing Skill: Letter Writing: formal and informal, Report writing; Business communication and tenders, business letters, letters of opinion, application and CV writing, e-mail, memo, etc.	CO1
2.	Listening Skill and Note Taking: Listening to recorded texts and class lectures and learning to take useful notes based on listening.	CO2
3.	Developing Speaking Skill: Oral skills including communicative expressions for personal identification, life at home, giving advice and opinion, instruction and directions, requests, complaints, apologies, describing people and places, narrating events.	CO3
4.	Discussion: A group of students to be brought on the dais at a time. Other students of the class will be interrogating and likewise every student should be brought in turn and questions should be asked from the fields of literature, science, current politics, international affairs, games and sports, etc. The Instructor will act as a conductor.	CO3

Text Books, Reference Books and Other Resources:

English Grammar in Use by Raymond Murphy.
Cambridge English advanced vocabulary
Cambridge English for IELTS

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2, A2	Lecture notes, PPT slides, web content	Class Test, Assignment, Midterm, Final Exam
CO2	PO1	C2, A2	Lecture notes, PPT slides, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO9, PO10	C3, A2	Lecture notes, PPT slides, web content	Class Test, Assignment, Midterm, Assignment, Final Exam

Course Title: Technical Writing & Presentation

Course Code: ENG 401

Credits: 2	Class Hours/Week: 2
Course Type: Language	Pre-requisite: GE
CIE Marks: 60	SEE Marks: 40

Course Rationale:

The course aims to equip students with the skills and knowledge necessary to effectively communicate technical information in a clear, concise, and professional manner. In today's highly interconnected and information-driven world, the ability to convey complex technical concepts to various audiences is crucial in fields such as engineering, computer science, business, and science.

Course Objectives:

The objectives of this course are:

To develop the skills necessary to communicate technical information clearly, concisely, and accurately to various audiences.
To develop skills in collaboration, communication, and negotiation to produce cohesive and unified documents and presentations.
To acquire skills in designing and delivering effective visual presentations using tools like PowerPoint or other presentation software.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Use (C3) and select (A2 ) appropriate words to make sentence and then paragraph, reports etc.
CO2	Use ( C3 ) and practice ( A2 ) modern tools to prepare technical and scientific documents.
CO3	Use ( C3) and explain (A4 ) information effectively.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

C O1

√

C O2

√

C O3

√

Course Description:

SL. No.	Course Content	COs
1.	Beginning to Write: a) Making sensible sentences. b) Joining and expanding sentences. c) Contracting sentences. d) Logical development of sentences in context using an idea. e) Clear and effective communication of information.	CO1, CO2, CO3
2.	Reading for Writing: Students will be required to comprehend modern prose-passages drawn from different disciplines with attention to their (a) context, (b) vocabulary, and (c) syntax, and deliver feedback in the form of précis, summaries, and comprehension answers. They will also be required to present their writings before the class for discussion and reactions by the peers.	CO1, CO2, CO3
3.	Expanding Writing: a) Writing paragraphs on technical aspects. b) Writing short, free and guided compositions. c) Developing essays on technical issues. d) Writing reports, memos and business letters. e) Editing compositions for clarity and effectiveness.	CO1, CO2, CO3

Text Books, Reference Books and Other Resources:

Engineer’s Guide to Technical Writing by Kenneth G. Budinski
Thinking on Paper by J.H. Barton and V.A. Howard

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO9, PO10	C3, A2	Lectures notes, PPT slides, Audio/Video content, Problem Solving	Group Work, Class Test, Assignment, Presentation, Viva
CO2	PO5, PO9, PO10	C3, A2	Lectures notes, PPT slides, Audio/Video content, Editing Software, Problem Solving	Group Work, Class Test, Assignment, Presentation, Viva
CO3	PO9, PO10	C3, A4	Lectures notes, PPT slides, Audio/Video content, Problem Solving	Group Work, Class Test, Assignment, Presentation, Viva

Mathematics

Course Title: Engineering Mathematics I

Course Code: MAT 105

Credits: 3	Class Hours/Week: 3
Course Type: Mathematics	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

It is essential for the engineering/science students to develop strong analytic skills which cannot be achieved without learning calculus of one and multiple variables. This course is intended to build up strong base in calculus as well as to include the experience of applications of its contents in the relevant courses of science/engineering.

Course Objectives:

The objectives of this course are:

To enhance the understanding and problem-solving skills on related/necessary basic mathematical topics required for learning ordinary and partial differentiation.
To develop the basic concepts of differentiation in its wide scope.
To build up problem solving experience in each relevant calculus topic along with addition of application familiarity in the related areas of science/engineering.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Explain (C2) different ways of representing functions: graphical, numerical, analytical or verbal and understand the connection among these representations.
CO2	Use (C3) derivatives to solve a variety of problems.
CO3	Illustrate (C3) series expansion of functions and apply series concept in engineering problems.
CO4	Calculate (C3) geometric properties and relationships to solve multistep problems in two dimensions.

Mapping of Course Outcomes to Program Outcomes:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Function, Various types of functions, Domain and range of the function, Graphical representation of function, Limit of function, Continuity of function	CO1
2.	Differentiability of function	CO1, CO2
3.	Differential co-efficient of various types of functions	CO2
4.	Successive Differentiation	CO2, CO3
5.	Leibnitz Theorem, Exercises on Leibnitz Theorem	CO1, CO2, CO3
7.	Roll’s Theorem and exercises, Mean value theorem and exercises	CO1
8.	Expansion of functions	CO3
9.	Euler’s Theorem	CO2
10.	Partial Differentiation	CO3
11.	Transformation of co-ordinate axes, Pair of straight lines, equations of conics and reduction to standard forms, Tangent, Normal, Asymptote, Second degree equation	CO4

Text Books, Reference Books and Other Resources:

Calculus—A New Horizon: By Howard Anton
The Elements of Coordinate Geometry: by S. L. Loney
Calculus & Analytic Geometry: by George B Thomas, Jr.
The Analytical Geometry of the Conic Sections: by The Rev. E. H. Askwith

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment tools
CO1	PO1, PO2	C2	Lecture, Notes, Problem solution	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam
CO4	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam

Course Title: Engineering Mathematics II

Course Code: MAT 107

Credits: 3	Class Hours/Week: 3
Course Type: Mathematics	Pre-requisite: EM I
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course aims to build up basics in integral calculus & linear differential equations in the context of engineering applications.

Course Objectives:

The objectives of this course are:

To help the students to determine length, area & volumes using integration.
To develop the ability to solve the differential equations in the context of engineering problems.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1

Determine (C2) indefinite and definite integral of algebraic, exponential, logarithmic, inverse functions.

CO2

Apply (C3) integration to calculate Arc, Areas of regions in a plane, Volumes of solids, surface area of solid revolution.

CO3

Identify (C2) order and degree of the differential equation. Distinguish between linear, non- linear, homogenous and Exact differential equation also solve 1 ^st order and 1 ^st degree differential equation.

Mapping of Course Outcomes to Program Outcomes:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Integration of various types of functions, Substitution method, Integration by Parts, Properties of definite integral, Beta and Gamma function	CO1
2.	Integration of various types of functions, Substitution method Integration by Parts, Properties of definite integral, Beta and Gamma function, Rectification, Area of a surface, Volume of solids of revolution	CO2
3. .	Integration of various types of functions, Substitution method, Integration by Parts, Properties of definite integral, Beta and Gamma function, Rectification Area of a surface, Volume of solids of revolution, Order and Degree of differential equation, Variable separable, Homogenous differential equation, Linear differential equation Exact differential equation solution of linear differential equations with constant coefficients series solution of differential equations	CO3

Text Books, Reference Books and Other Resources:

Calculus & Analytic Geometry: by George B Thomas, Jr.
Differential Equations: by Erinu Kresgige
Advanced Engineering Mathematics: by Peter V. O’Neil
An Introduction to Differential Equation and Tensor Analysis: by Syed Md. Farid

Ordinary and Partial Differential Equations: by M D. Raisinghania

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Notes, Problem solution	Assignment, Class Test, Midterm Exam, Final Exam
CO2	PO1	C3	Lectures, Notes, Practice Problems	Assignment, Class Test, Midterm Exam, Final Exam
CO3	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Midterm Exam, Final Exam

Course Title: Engineering Mathematics III

Course Code: MAT 201

Credits: 3	Class Hours/Week: 3
Course Type: Mathematics	Pre-requisite: EM II
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is intended to build up strong bases in the complex number system and its relevant calculus, in vector calculus, and in statistics and probability, as well as to include the experience of applications of its contents in the relevant courses of science/engineering.

Course Objectives:

The objectives of this course are to:

Enhance and broaden the contents covered in pre-requisite math courses, and builds up a moderately strong base in statistics and probability.
This course also highlights the basic concept of vector calculus and Statistics (Probability, Distribution, Sampling, etc.).
While doing the above it builds up problem solving experience in each relevant topic along with the addition of application familiarity in the related areas of science/engineering.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C2) complex analysis techniques for geometric interpretation.
CO2	Calculate (C3) vector field parameters using vector integration.
CO3	Comprehend (C2) the formulation of statistical data from various distributions.
CO4	Analyze (C4) real-world problems using various probability models.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

C O1

√

C O2

√

C O3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Complex Number System, Analytic Function, Harmonic Function Cauchy-Riemann theorem, function, exercise Construction of analytic functions, Cauchy’s theorem, Cauchy’s Integral formula, exercises Cauchy’s Residue theorem, Exercises on Series Theorem.	CO1
2.	Vector Differentiation, Del operator, gradient, divergence, curl, Laplacian operator, Line, Surface and Volume integral, Exercises, Gauss’s Theorem, exercises, Green’s Theorem, exercises, Stoke’s theorem, exercises	CO2
3.	Basic concepts of frequency distribution, Measures of location and variation Permutation, Combination	CO3
4.	Probability Distribution, Binomial, Poisson’s Distribution	CO4

T e xt Books, Reference Books and Other Resources:

A Text Book on Co-ordinate Geometry with Vector Analysis: By A. F. M. Abdur Rahman and P. K. Bhattacharjee
Advanced Engineering Mathematics: by Peter V. O’Neil
Basic Statistics & Probability: by S. K. Gupta
An Introduction to Statistics and Probability: by M. Nurul Islam
Theory and Problems of Complex Variables: by Murray R. Spiegel

M apping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Problem solution	Assignment, Class Test, Mid Term, Final
CO2	PO1	C3	Lecture notes, Problem solution	Assignment, Class Test, Mid Term, Final
CO3	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final
CO4	PO1	C4	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final

Course Title: Engineering Mathematics IV

Course Code: MAT 203

Credits: 3	Class Hours/Week: 3
Course Type: Mathematics	Pre-requisite: EM III
CIE Marks: 60	SEE Marks: 40

Course Rationale:

It is essential for the engineering/science students to develop strong analytic skills which cannot be achieved without extending knowledge and skills obtained in the mathematics courses (MAT 105, MAT 107, MAT 201) and without including matrix methods in particular and Linear Algebra in general. This course is intended to build up strong bases of Laplace transforms, Fourier analysis, partial differential equations (P. D. E.), matrix, and Linear Algebra, as well as to include the experience of applications of its contents in the relevant courses of science/engineering.

Course Objectives:

The objectives of this course are to:

This course enhances and broadens the contents covered in MAT 105, MAT 107, & MAT 201.
Toward the above goal, it develops the basic concepts of Laplace transforms, Fourier analysis, matrix, and Linear Algebra in their wide scopes, and of P. D. E in its relevant scopes.
While doing the above it builds up problem solving experience in each relevant topic along with the addition of application familiarity in the related areas of science/engineering. Etc.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Apply (C3) different types of matrices and determinants in solving real time engineering problems.
CO2	Solve (C3) different types of matrix and linear transformation.
CO3	Apply (C3) Fourier analysis to convert time domain signal to frequency domain signal and vice-versa.
CO4	Solve (C3) basic properties, inverse properties, differential and integral equations using Laplace Transformation.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

C O1

√

C O2

√

C O3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Basic definition of matrix, Inverse and Rank of a matrix Test of solution system, State and prove Cayley-Hamilton theorem & verify some matrix	CO1
2.	Eigen values and Eigen vectors, Applications of Eigenvalues and Eigenvectors, Geometrical meaning of Linear Independence and discussion on CT answer script, Application, Vector Space and Subspace, Linear combination, Application	CO2
3.	Formulation of Fourier series, Formulation of Fourier series, Some example of Fourier series with graph, Review	CO3
4.	Some important properties of Laplace transformation and calculate problems, Established basic formula of Laplace transformation in different function, Some important properties of Laplace transformation and calculate problems, Laplace transform of first derivative to n ^th derivatives, Definition of inverse Laplace transform and calculate problems, Solution of initial value problem by Laplace transform, solution of integral equation by Laplace transform Application	CO4

Text Books, Reference Books and Other Resources:

Linear Algebra By Howard Anton
Mathematical Physics By B.D.Gupta
Linear Algebra by Abdur Rahman
Linear Algebra & Its Applications by David C. Lay
Advanced Engineering Mathematics by Peter V. O’Neil
Fourier Series and Boundary Value Problems by Charchil
Laplace Transform by M. R. Spigel

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Lecture, Notes, Problem solution	Class Test, Midterm, Assignment, Final
CO2	PO1	C3	Lectures, Notes, Practice Problems	Class Test, Midterm, Assignment, Final
CO3	PO1	C3	Lectures, Notes, Practice Problems	Class Test, Midterm, Assignment, Final
CO4	PO1	C3	Lectures, Notes, Practice Problems	Class Test, Midterm, Assignment, Final

Core Courses

Course Title: Introduction to Computer Systems Laboratory

Course Code: CSE 110

Credits: 2	Class Hours/Week: 4
Course Type: Core	Pre-requisite:
CIE Marks: 40	SEE Marks: 60

Course Rationale:

This course is to introduce the basic computer system and computer programming principles.

Course Objectives:

The objectives of this course are:

To introduce the fundamentals of computer systems.
To help students hands-on use of: Word processing, Spreadsheet, and Presentation applications.
To familiarize with number systems.
To introduce basic programming language.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Define (C1) the fundamental concepts of a computer system and number systems.
CO2	Use (C3) word processing, spreadsheet, and ppt presentation for solving engineering tasks.
CO3	Execute (C3) basic computer programming principles like data types, programming constructs, input and output operations.
CO4	Report (A3) lab results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Introduction: Generation of Computer, Functions of the Different Computer Units, Input & Output Devices, Computer Memories and Software.	CO1, CO4
2.	Number Systems: Binary, Octal, Decimal and Hexadecimal.	CO1, CO4
3.	Office applications: MS Word, MS Excel, MS Power Point	CO2, CO4
4.	Introduction to C Language: Flow Charts, Identifiers, Data Types, Variables, Constants, Input/output Statements, Operators and Expressions, Conditional Statements (If, else, switch, etc.), Looping statements (for, while, do-while), arrays. Flow Charts, Arithmetic Operators and Expressions: Evaluating Expressions, Precedence and Associativity of Operators, Type Conversions. Flow Charts, Conditional Control Statements: Bitwise Operators, Relational and Logical Operators. Flow Charts, Conditional Control Statements: If, else If. Flow Charts, Conditional Control Statements: Switch Statement and Examples. Flow Charts, Looping Statements: For, While and Do-While Statements with Examples.	CO3, CO4

Text Books, Reference Books and Other Resources:

Introduction to computers ---- (Peter Norton)
C- The Complete Reference ---- (Herbert Schildt)
Schaum's Outline of Programming with C ---- (Byron S. Gottfried)
Programming in ANCI C ---- (E. Balagurusami)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C1	Lecture notes, PPT slides, problem solving, web content	Lab Performance
CO2	PO1	C3	Lecture notes, PPT slides, problem solving, web content	Lab Performance
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Lab Performance, Lab Exam
CO4	PO10	A3	Demonstration	Report

Course Title: Electrical Circuit I

Course Code: EEE 101

Credits: 3	Class Hours/Week: 3
Course Type: Core	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is intended to enable the students to learn the fundamentals of electrical circuits. Use the acquired knowledge to understand the working and operations of electrical circuits to solve the problems for industrial opportunities.

Course Objectives:

The objectives of this course are:

To provide the knowledge of Direct Current (DC) and Alternating Current (AC) Circuits.
Helping the students to develop the ability to solve the unknown electrical parameters which are used to design electrical circuits.
To develop skills to analyze the behavior of electrical circuits.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Demonstrate (C3) the electrical parameters which are used to design electrical and electronic devices.
CO2	Illustrate (C3) the circuit laws, network analysis and methodologies to solve DC as well as AC circuits/networks.
CO3	Solve (C3) different types of circuit theorems to find the unknown parameters of an electrical circuit/network.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Circuit Theorems: Fundamental electrical concepts, Electrical parameters in AC and DC circuits, Measuring devices in the electrical system, Different types of materials, Temperature effect on materials, Ohm’s Law, KVL, KCL, VDR, CDR.	CO1, CO2
2.	DC Circuits (DC): Series DC circuits, Parallel DC Circuits, Series-parallel circuits.	CO1, CO2
3.	Method of Analysis and Theorems: Different types of circuit conversions, Branch Current analysis, Mesh analysis, Nodal analysis, Network theorems.	CO2, CO3
4.	AC Circuits (AC): The Basic Elements and Phasors: R branch, L branch, C branch, and RLC branch Series AC circuits, Parallel AC circuits, Series-parallel AC circuits. Reasoning Circuit: Series and Parallel circuits.	CO1, CO2

Text Books, Reference Books and Other Resources:

Fundamentals of Electric Circuits, Charles K. Alexander & Matthew N. O. Sadiku
Introductory Circuit Analysis, Robert L. Boylestad

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Electrical Circuit I Laboratory

Course Code: EEE 102

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This course is intended to enable the learners to analyze the behavior of electrical circuits, use the acquired knowledge to implement or design efficient electrical circuits practically to solve real world problems.

Course Objectives:

The objective of the course is to enable the students to

To provide practical knowledge of the electrical circuits as well as the technical skills to implement such electrical circuits.
To help the students to pursue further studies in electrical or telecommunications engineering as well as some other related engineering disciplines including computer engineering.
This course covers practical works that will be helpful for students to build and analyze devices in real world.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Execute (C3) the circuit network and theorems related to DC and AC circuits/networks.
CO2	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

Course Description:

SL

No.

Course Content

COs

1

Laboratory work based on theory course EEE 1101: Introduction to Electrical Engineering

CO1, CO2

Text and Reference books:

Electrical Circuit Lab Manual Book

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs

Corresponding POs

Bloom’s Taxonomy Domain/Level

(C: Cognitive, P: Psychomotor A: Affective)

Delivery Methods and Activities

Assessment Tools

CO1

PO1, PO2

C3

Demonstration, discussion, experiment

Quiz/Written Exam, Performance

CO2

PO10

A3

Demonstration

Report

Course Title: Mechanical Engineering Drawing & CAD Laboratory

Course Code: ME 102

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite:

CIE Marks: 40 SEE Marks: 60

Course Rationale:

This course leads students to an understanding of engineering drawing, an essential means of communication in engineering.

Course Objectives:

The main objectives of this course are:

Familiarization and use with drawing instruments and conventions in making engineering drawings.
Freehand single view and multi-view sketching.
Isometric sketching techniques and multi-view projections.
Tolerance and dimensioning and applying them in drawing exercises.
Use of AutoCAD (Or Solid Works) in making engineering drawings with dimensions and taking sectional views.
Drawing, manipulation and modification of solids using AutoCAD (Or Solid Works) Extraction of multiple views from a solid model in AutoCAD (Or Solid Works).

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Use (C3) drawing instruments in making an engineering drawing.
CO2	Describe (C2) different types of lines, drawing paper sizes, and grades of pencils.
CO3	Illustrate (C3) freehand single view, multi-view sketches, and isometric sketches.
CO4	Use (C3) AutoCAD (Or Solid Works) to draw plane drawing.
CO5	Demonstrate (C3) multi view projections using AutoCAD (Or Solid Works).
CO6	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

CO5

√

CO6

√

Course Description:

SL No.	Course Content	COs
1.	This course contributes towards the engineering topics component of the mechanical engineering curriculum by familiarizing students with the state-of-the-art CAD and FEA software for modeling, analyzing and designing mechanical components. This course brings into focus the Introduction, Orthographic drawings, First and third angle projections, scale drawing, sectional view, isometric views, missing line, auxiliary view, detail and assembly drawing, project on engineering drawing and CAD using AutoCAD or contemporary packages instructed by the teachers. The students also understand the requirements for good engineering drawings, and are able to apply these to their work/projects.	CO1, CO2, CO3, CO4, CO5, CO6

Text Books, Reference Books and Other Resources:

AutoCAD – 2002 instant reference, by Goerge Omura & B. Robert Colloria.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C3	Demonstration, PPT slides	Lab performance, Quiz, Lab Exam
CO2	PO1, PO2	C2	Demonstration, PPT slides	Lab performance, Quiz, Lab Exam
CO3	PO1, PO2	C3	Demonstration, PPT slides	Lab performance, Quiz, Lab Exam,
CO4	PO1, PO2, PO5	C3	Demonstration, PPT slides	Lab performance, Quiz, Lab Exam,
CO5	PO1, PO2, PO5	C3	Demonstration, PPT slides	Lab performance, Quiz, Lab Exam,
CO6	PO10	A3	Demonstration	Report

Course Title: Discrete Mathematics

Course Code: CSE 103

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course will introduce the basic elements of discrete mathematics for an understanding of

algorithms and data structures used in computing.

Course Objectives:

The main objectives of this course are:

To acquaint students with a good understanding of the concepts and methods of discrete mathematics
Helping the students to develop ability in evaluating elementary mathematical arguments and analyzing reasons.
To help the students to apply appropriate mathematical concepts and operations to interpret data in problem solving.
To Foster the students to enhance logical thinking for demonstrating the basic algorithms.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C3) a solid understanding of fundamental concepts in discrete mathematics.
CO2	Prove (C2) elementary properties of modular arithmetic and explain their applications in computer science.
CO3	Apply (C3) graph theory models of data structures and state machines to solve problems of connectivity and constraint satisfaction.
CO4	Comprehend (C2) basic concepts in formal language and computability.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Graph theory problem and applications	CO3
2.	Basic number theory and modular arithmetic	CO2
3.	Propositional and Predicate Logic	CO4
4.	Method of Proofs	CO1

Text Books, Reference Books and Other Resources:

Discrete Mathematics and Its Applications, Seventh Edition by Kenneith H. Rosen
Discrete Mathematics, Seventh Edition by Richard Johnson

Mapping Course Outcomes with the Teaching – Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Lecture, Problem solution, Web link, PPT slides	Assignment, Class Test, Midterm Exam, Final Exam
CO2	PO2	C2	Lecture, Problem solution, Web link, PPT slides	Assignment, Class Test, Midterm Exam, Final Exam
CO3	PO2, PO3	C3	Lecture, Problem solution, PPT slides, Web link	Assignment, Class Test, Midterm Exam, Final Exam
CO4	PO1	C2	Lecture, Problem solution, PPT slides Web link	Assignment, Class Test, Midterm Exam, Final Exam

Course Title: Structured Programming

Course Code: CSE 111

Credits: 2 Class Hours/Week: 2

Course Type: Core Pre-requisite: ICS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course introduces computer programming and problem solving in a structured program logic environment.

Course Objectives:

The objectives of this course are:

To introduce the student with fundamental principles, typical characteristics and mechanism of structured programming.
To introduce students with the algorithmic way of thinking and problem solving by computers.
To help the student to enhance their analysing skills and use the same for writing program in C.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Comprehend (C2) the fundamental concepts of structured programming such as variables, data types, operators, and control statements.
CO2	Apply (C3) fundamental programming constructs such as control structures, functions, dynamic memory allocation, file management to solve programming problems.
CO3	Develop (C3) modular and reusable code by implementing user-defined functions and data types.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Basic overview of C tokens, Variable, Variable declaration, Operators, input / output, opening and closing a file, creating a file, processing a file.	CO1
2.	Control Statement: Decision Making and Branching (if-else, nested if-else, switch-case, goto)	CO2
3.	Control Statement: Looping (for, while, do-while, nested looping), break and continue statement	CO2
4.	One-dimensional Array, Multidimensional array, String, Array of Strings, user defined function, recursion function, pointers, structure & union.	CO2, CO3

Text Books, Reference Books and Other Resources:

Programming in ANSI C. (Latest Edition) ---- (E. Balagurusamy)
Teach Yourself C –-- (Herbert Schild)
Programming with C ---- (Schaum’s Outlines)
The C Programming Language ---- (C Kernighan & D.M. Ritchie)
Theory and Problems of Programming with C ---- (Byron S. Gottfried)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture notes, PPT slides, problem solving	Assignment, Class Test, Midterm Exam, Final Exam
CO2	PO1, PO2	C3	Lecture notes, PPT slides, problem solving	Assignment, Class Test, Midterm Exam, Final Exam
CO3	PO1	C3	Lecture notes, PPT slides, problem solving	Assignment, Class Test, Midterm Exam, Final Exam

Course Title: Structured Programming Laboratory

Course Code: CSE 112

Credits: 2 Class Hours/Week: 4

Course Type: Core Pre-requisite: ICS

CIE Marks: 40 SEE Marks: 60

Course Rationale:

Intended to familiarize with various techniques of programming aimed at developing the skills of analyzing and solving problems by using functions/methods, block structures, control statements, file etc.

Course Objectives:

The objectives of this course are:

To introduce the student's various concepts and structures for writing programs.
To evaluate the opportunities to develop basic programming skills with respect to program design and development.
To develop logical and analytical ability through problem solving.
To make the students interested in Competitive Programming.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C2) a sound understanding of fundamental programming concepts, including variables, data types, control structures, functions, and arrays.
CO2	Apply (C3) different types of structured programming tools to solve real world problems.
CO3	Debug (C3) structured programs to identify and correct errors.
CO4	Analyze (C4) codes to improve readability, maintainability, and efficiency.
CO5	Describe (A3) the importance of Competitive Programming like IUPC, NCPC, ICPC etc.
CO6	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

CO5

√

CO6

√

Course Description:

SL No.	Course Content	COs
1.	Compiler/IDE setup, Basic input/output, file-handling, C program without using conditional and loop statements	CO1, CO6
2.	Problem solving using Control Structures and loops (if-else, nested if-else, switch-case, for loop, nested for loops, while loops, do-while loops), one dimensional array and multi-dimensional array, string processing, user defined functions, structure and unions.	CO1, CO2, CO6
3.	Where Do Errors Come from in Coding? Common programming errors and how to handle them. Error handling, Debugging and Testing	CO3, CO6
4.	Reusability, Maintainability, Scalability and performance	CO4, CO6
5.	Introduction to competitive programming (NCPC, ICPC, Online Judge, Contest criteria etc.), problem solving in different online platforms.	CO5, CO6

Text Books, Reference Books and Other Resources:

Online references
Programming in ANSI C. (Latest edition) ---- (E. Balagurusamy)
Schaum's Outline of Programming with C (Schaum's Outline Series) ---- (Byron Gottfried, Latest Edition)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Demonstration, Discussion, Experiment	Lab Performance, Lab Exam, Quiz
CO2	PO1, PO5	C3	Demonstration, Discussion, Experiment	Lab Performance, Lab Exam, Quiz
CO3	PO5	C3	Demonstration, Discussion, Experiment	Lab Performance, Lab Exam, Quiz
CO4	PO2	C4	Demonstration, Discussion, Experiment	Lab Performance, Lab Exam, Quiz
CO5	PO1, PO12	A3	Demonstration	Programming Contest Problem Solving
CO6	PO10	A3	Demonstration	Report

Course Title: Electronics I

Course Code: EEE 211

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: ECKT I

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course is focused on beginners to provide foundation knowledge of electronics devices that are used in modern day electronic system.

Course Objectives:

The objectives of this course are -

To understand the basic knowledge of electronics and devices.
To provide foundation knowledge to solve unknown parameters of electronic circuits
and devices.
To develop skills to design electronic circuits/networks.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Explain (C2) the knowledge about electronic devices and parameters.
CO2	Interpret (C2) of electronics parameters that are used to design electronics devices.
CO3	Apply (C3) the problems related to electronic systems and designs.
CO4	Illustrates (C3) electronics-related circuitry and its interfacing.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	P-N junction as a circuit element: Intrinsic and extrinsic semiconductors, operational principle of p-n junction, contact potential, current-voltage characteristics of a diode, simplified dc and ac diode models, dynamic resistance and capacitance.	CO1, CO2, CO3
2.	Diode circuits: Half wave and full wave rectifiers, rectifiers with filter capacitor, characteristics of a zener diode, zener shunt regulator, clamping and clipping circuits.	CO1-CO4
3.	Bipolar junction transistor: Current components, BJT characteristics and regions of operation, BJT as an amplifier, biasing the BJT for discrete circuits, small signal equivalent circuit models, BJT as a switch. Single stage mid-band frequency BJT amplifier circuits and different type of biasing techniques. Voltage and current gain, input and output impedance of common base, common emitter and common collector amplifier circuits.	CO1, CO2, CO4
4.	Metal-oxide-semiconductor field-effect-transistor (MOSFET): Structure and physical operation of an enhancement MOSFET, threshold voltage, Body effect, current- voltage characteristics of an enhancement MOSFET biasing discrete and integrated MOS amplifier circuits, Current Mirror: Simple CMOS current mirror, Source degenerated current mirror, high output impedance current mirror, Bipolar current mirror; Single-stage MOS amplifiers, MOSFET as a switch, CMOS inverter.	CO1- CO4
5.	Junction field-effect-transistor (JFET): Structure and physical operation of JFET, transistor characteristics, pinch-off voltage. Differential and multistage amplifiers, Description of differential amplifiers, small-signal operation, differential and common mode gains, RC coupled mid-band frequency amplifier.	CO1-CO4
6.	Active filters: Basics of Op-Amp, its characteristics, Different types of Op-Amp	CO1, CO3, CO4

Text Books, Reference Books and Other Resources:

Electronics Devices and Circuit Theory by Robert L. Boylestad & Louis Nashelsky.
Electronics Devices by Thomas L. Floyd.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Problem solving, PPT slide	Assignment, Class Test, Midterm Exam, Final Exam
CO2	PO1	C2	Lecture, Problem solving, PPT slide	Assignment, Class Test, Midterm Exam, Final Exam
CO3	PO1	C3	Lecture, Problem solving, PPT slide	Assignment, Class Test, Midterm Exam, Final Exam
CO4	PO1	C3	Lecture, Problem solving, PPT slide	Assignment, Class Test, Midterm Exam, Final Exam

Course Title: Electronics I Laboratory

Course Code: EEE 212

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: ECKTL

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This laboratory course is focused on the practical aspects of the course Electronics I course.

Course Objectives:

The objectives of this course are:

Familiarize with the different component of electronics system such as diode, transistor
To provide adequate knowledge on electronic devices.

To make students learn how to use the measurement equipment for electronics system.
Test the physical characteristics of different component in lab.
Develop some practical circuits that describe the basic phenomenon of electronics.
Provide design concept of complex electronics system.
Introduce with the design issue of the electronic system.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1

Demonstrate (C2, P2) different electronic circuits and power supply as well as the

BJT amplifier/JFET amplifier, and their operation.

CO2

Conclude (C5) and show (P3) the result from experimental data.

CO3

Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

Course Description:

SL No.	Course Content	COs
1.	Familiarization with the components & devices used in Electronics Lab	CO1
2.	Find out the v-i characteristics of a semiconductor diode under forward & reverse biased condition	CO1, CO2, CO3
3.	Design a Half wave rectifier	CO1, CO2, CO3
4.	Design a Full wave rectifier using center taped transformer.	CO1, CO2, CO3
5.	Design a Full wave bridge rectifier.	CO1, CO2, CO3
6.	Find out the transistor characteristics curve for common emitter configuration.	CO1, CO2, CO3
7.	Find out the transistor characteristics curve for IB vs VBE.	CO1, CO2, CO3
8.	Find out the transistor characteristics curve for common base configuration.	CO1, CO2, CO3
9.	Single stage Common emitter configured NPN transistor amplifier.	CO1, CO2, CO3
10.	Observation of Common collector configured NPN transistor amplifier	CO1, CO2, CO3
11.	Observation of FET performance	CO1, CO2, CO3

Text Books, Reference Books and Other Resources:

Lab Manual

CO Delivery and Assessment:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2, P2	Lecture & Laboratory Experiments	Quiz, Performance Test, Report
CO2	PO4	C5, P3	Lecture & Laboratory Experiments	Performance Test, Report
CO3	PO10	A3	Demonstration	Report

Course Title: Object Oriented Programming

Course Code: CSE 211

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To empower the learner to perceive the fundamental knowledge of object-oriented programming paradigm and aimed at developing the skills of analyzing and solving real-world problems.

Course Objectives:

The objectives of this course are to:

Build an understanding of the object-oriented approach in programming.
Familiarize the student with the writing of computer programs to solve real-world problems in Java.
To make students learn and appreciate the importance and merits of proper comments in source code and API documentation.
Be able to write GUI interfaces for a computer program to interact with users, and to understand the event-based GUI handling principles.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Explain (C2) the fundamental OOP concepts (Classes, Operators, Variables, Keywords, Objects, Methods, Constructors, and Packages).
CO2	Illustrate (C3) different object-oriented principles (Encapsulation, Polymorphism, Abstraction and Inheritance) to solve engineering problems.
CO3	Demonstrate (C3) object-oriented features: exception handling, multi-threading, generics, collection framework, file handling, and GUI.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction: Basic concepts on object-oriented programming (OOP), benefits and application areas of Object-Oriented Programming, procedural vs. OOP programming, Comparison with OOP and other languages paradigm, and important features of OOP	CO1
2.	Class, objects, and constructor: Introducing classes, objects and constructors (class fundamentals, declaring objects, assigning object reference variables)	CO1
3.	Method basics and method overloading: Introducing Methods, this Keyword, Garbage Collection, the finalize () Method, Overloading Methods, using objects as parameters, returning objects, constructor, and constructor overloading.	CO1, CO2,
4.	Inheritance: Method overriding, Inheritance, different types of inheritance	CO2
5.	Miscellaneous class: Understanding Static, Introducing Super, Final, Nested, abstract, wrapper, and Inner class	CO1, CO2
6.	Interface: Basics of interface, class vs. interface, multiple inheritances, defining an interface, dynamic initialization	CO2
7.	Multithreaded Programming: Basics of thread, differences between multithreading and multitasking, the concept of multithreaded programming, and different ways to create a new thread.	CO3
8.	Multithreaded Programming: Implementing the Runnable interface, the life cycle of a thread, deadlock, synchronization, and set thread’s priority.	CO3
9.	Managing Errors and Exceptions: Explain different types of errors, Errors vs. Exceptions, and common Java exceptions with the cause of occurrence. Exception handling mechanism, finally blocks, illustrating the usage of throw and throws, user-defined exception.	CO3
10.	Generics: Basic concepts of generics in Java, advantages and use cases.	CO3
11.	Java Graphics: Enumerate the basic concepts of the Swing package.	CO3
12.	Event Handling and Layout Managers: AWT (Abstract Window Toolkit), Delegation Event Model, Event Listeners, and Sources of Events. Enumerate various types of event classes and listener interfaces. Adapter classes. Basics of the layout manager. Discuss different types of layout managers.	CO3
13.	File Handling (Input/Output): Stream in Java, the hierarchy of java stream classes, and purposes of the various stream functions. Stream Tokenizer, random and sequential access file, basic file-related programs to check the I/O. Basic file-related programs to check the I/O.	CO3
14.	Java Collections Framework: Java Map methods e.g., tree map, hash map. relevant programs. Java collections framework and iterator. relevant programs. List, set, and relevant programs.	CO3

Textbooks, Reference Books and Other Resources:

Programming with Java A Primer--- (E. Balagurusamy)
Java the Complete Reference --- (Herbert Schildt)
Java How to Program--- (Deitel and Deitel)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Object Oriented Programming Laboratory

Course Code: CSE 212

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SPL

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To empower the learner to perceive the fundamental knowledge of object-oriented programming paradigm and aimed at developing the skills of analyzing and solving real-world problems.

Course Objectives:

The objectives of this course are to:

Build an understanding of the object-oriented approach in programming.
Familiarize the student with the writing of computer programs to solve real-world problems in Java.
To make students learn and appreciate the importance and merits of proper comments in source code and API documentation.
Be able to write GUI interfaces for a computer program to interact with users, and to understand the event-based GUI handling principles.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C3) the fundamental OOP concepts (Classes, Objects and Packages) using Object Oriented Language.
CO2	Implement (C3) different object-oriented principles (Encapsulation, Polymorphism, Abstraction and Inheritance) to solve engineering problems.
CO3	Implement (C3) object-oriented features: exception handling, multi-threading, generics.
CO4	Implement (C3) GUI features to develop lightweight application based on constraints.
CO5	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

CO4

✓

CO5

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction: Basics of Java programming language, equation solving, conditional statement-related problem solving, and loop	CO1, CO5
2.	Class, objects, and constructor: Programs related to Class, Objects and Constructor	CO1, CO5
3.	Method overloading, Method Overriding, Inheritance, Abstraction, Polymorphism, Encapsulation	CO2, CO5
4.	Multithreaded Programming: Programs related to multithreading, creating a new thread, implementing the Runnable interface, synchronization, and setting the thread’s priority.	CO3, CO5
5.	Managing Errors and Exceptions: Programs related to errors and exceptions, common Java exceptions, exception handling mechanism, and finally block, illustrating the usage of throw and throws, user-defined exception.	CO3, CO5
6.	Applet: HTML and applet program integration, graphics programming with the applet, repaint () method, Swing package, Event Handling and Layout Managers	CO4, CO5

Text Books, Reference Books and Other Resources:

Programming with Java A Primer--- (E. Balagurusamy)
Java the Complete Reference --- (Herbert Schildt)
Java How to Program--- (Deitel and Deitel)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO2	PO1, PO2	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO3	PO1	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO4	PO1, PO2	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO5	PO10	A3	Demonstration	Report

Course Title: Data Structures

Course Code: CSE 221

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Rationale:

Data structure provides a good understanding for organizing and storing data in a computer

such that it can be stored, retrieved, and updated frequently.

Course Objectives:

The main objectives of this course are to:

Make the students understand the data structures and their implementations.
Facilitate necessary knowledge about the importance of data structures in the context of writing efficient programs.
Develop the ability to implement appropriate data structures in problem solving.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Interpret (C2) the basic concepts of data structures, their types and basic operations.
CO2	Summarize (C2) the strength and weaknesses of different data structures.
CO3	Use (C3) the appropriate data structures in the context of the solution to any given problem.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction to Data Structures, Basic Operations, and Performance Measurement.	CO1
2.	Introduction to Array, Search, and Sorting Strings: Pattern Matching Algorithm (Naive and KMP)	CO1, CO2, CO3
3.	Types of Linked List and Basic Operations on Linked List	CO1, CO2, CO3
4.	Basic Operations on Stack and Use of Recursion Types of Queue and Basic Operations on Queue	CO1, CO2, CO3
5.	Types of Trees, Representation of Tree, Binary Search Tree, Balanced Search Tree, Efficient Heap	CO1, CO2, CO3
6.	Graph Representation, Traversing Graph, Topological Sorting, Path Matrix, Warshall Algorithm	CO1, CO2, CO3
7.	Hash Function and its Application.	CO1, CO2

Text Books, Reference Books and Other Resources:

Seymour Lipschutz, Data Structures, McGraw-Hill, 2014.
Debasis Samanta, Classic Data Structures, Prentice-Hall of India Private Limited, 2009.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery methods and activities	Assessment tools
CO1	PO1	C2	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final
CO2	PO2	C2	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final Examination
CO3	PO1, PO2	C3	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final Examination

Course Title: Data Structures Laboratory

Course Code: CSE 222

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SPL

CIE Marks: 40 SEE Marks: 60

Rationale:

To practically implement as well analyze the various data structures and basic algorithm

analysis.

Course Objectives:

The main objectives of this course are to:

Help the students to understand the analysis and design of linear and non-linear data structures in the context of writing efficient programs.
Develop the ability in identifying and applying the suitable data structure for the given

real- world problem.

Course Outcomes (COs):

CO1	Describe (C2) the operations of basic data structures.
CO2	Classify (C2) the appropriate data structure to solve specific problems.
CO3	Solve (C3) problems or improve existing code using learned data structures.
CO4	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

CO4

✓

Course Description:

SL

No.

Course Content

COs

1.

Identify and apply the basic operations of data structures: Array, Linked List, Stack, Queue, Tree, Graph.

CO1, CO4

2.

Identify the basic data structures and apply solutions: Bubble Sort, Selection Sort, Insertion Sort, Linear Search, Binary Search, Pattern matching: Naïve, KMP, Binary Search Tree, Heap Tree (Using Priority Queue), Breadth-first Search, Depth-first Search

CO2, CO3, CO4

Text Books, Reference Books and Other Resources:

Data Structures (Third Edition), by Seymour Lipschutz, Publishers: McGraw-Hill.
Classic Data Structures, by D. Samanta, Publishers: Prentic-Hall of India Private Limited.
Data Structures and Algorithms in Python, by Michael T. Goodrich, publisher: VP & PUBLISHER

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO2	PO2	C2	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO3	PO2	C3	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO4	PO10	A3	Demonstration	Report

Course Title: Digital Electronics

Course Code: EEE 311

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course aims to empower the learner to inspect and perceive the fundamental knowledge of digital electronics and develop sufficient background for advanced digital electronic courses.

Course Objectives:

The objectives of this course are to:

Provide a modern introduction to logic design and the basic building blocks used in technical areas in particular digital computers.
Familiarize the student with fundamental principles of 21st century’s digital design.
Provide coverage of classical hardware design for both combinational and sequential logic circuits.
Develop research skills and aspects of professional practice through group-bases assignments and team works that leads them to become successful self-employed, motivator, entrepreneur and manager.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) the fundamental concepts, theories and techniques used in digital electronics.
CO2	Interpret (C2) various combinational and sequential circuits.
CO3	Implement (C3) descriptions of logical problems to efficient digital logic circuits.
CO4	Demonstrate (C3) memory, interfacing and converter.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Number systems: Representation of numbers in different bases, Addition and subtraction in different bases, complement: Subtraction using complements, Binary multiplication & division.	CO1
2.	Binary codes: Different coding system, Boolean algebra, various gates, Sum of products and product of sums, Standard and canonical forms and other logical operations.	CO1
3.	Simplification of Boolean functions: Karnaugh map method, Tabular method of simplification; Implementation of logic circuit using various gates, Universal gates.	CO2
4.	Combinational logic circuit: Design procedure: Adder, Subtractor, Code converters, Parity bit checker and magnitude comparator, Analysis of different combinational circuits, Encoder, decoder, Multiplexer, Demultiplexer, ROM, PLA and their applications.	CO3
5.	Flip-flops: SR, JK, Master slave, T and D type flip-flops and their characteristic tables & equations; Triggering of flip-flops, Flipflop, Excitation table.	CO3
6.	Sequential circuits: Introduction to sequential circuits, Analysis and synthesis of synchronous and asynchronous sequential circuits.	CO3
7.	Counters: Classifications, Synchronous and asynchronous counter design and analysis, Ring counter, Johnson counters, Ripple counter and counter with parallel load.	CO3
8.	Registers: Classification, Shift registers, Circular registers and their applications and registers with parallel load. Basic Concept of Application Specific IC (ASIC) design.	CO3
9.	Digital IC logic families: Brief description of TTL, DTL, RTL, ECL, I2L, MOS and CMOS logic and their characteristics, principles of operation and application.	CO2
10.	Digital IC logic families: Brief description of TTL, DTL, RTL, ECL, I2L, MOS and CMOS logic and their characteristics, principles of operation and application.	CO2
11.	Memory Units: Various memory devices and their interfacing.	CO4
12.	Converters: Digital to Analog (D/A), Analog to Digital (A/D) converters, and their applications.	CO4

Text Books, Reference Books and Other Resources:

Digital Logic and Computer Design by M. Morris Mano
Modern Digital Electronics, by R.P. Jain.
Digital Electronics, by Tocci.
Computer Engineering by M. M. Mano,
Digital Computer Electronics by A.P. Malvino.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO2	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO3	PO1, PO2	C3	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO4	PO1, PO2	C3	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination

Course Title: Digital Electronics Laboratory

Course Code: EEE 312

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This subject aims to get acquainted with integrated circuits, to design and implement digital circuits, and to be able to troubleshoot implemented circuits.

Course Objectives:

The objective of the course is to enable the students to:

Make students understand the operations and applications of various digital electronic circuits.
Help students develop the analyzing skills of digital electronic circuits.
Help students develop the ability to present the results of investigations orally and in writing.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C3, P2) different logic gate circuits as well as the multiplexer/demultiplexer and counter operation.
CO2	Conclude (C4) and show (P3) the result from experimental data.
CO3	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Familiarization of Digital Electronics Laboratory	CO1
2.	Familiarization of the Digital Logic gates ICs, truth tables & verification of Fundamental logic gates.	CO1, CO2, CO3
3.	Boolean function implementation.	CO1, CO2, CO3
4.	Verification of universality of NAND & NOR gates	CO1, CO2, CO3
5.	Verification of Half adder & Full adder circuit.	CO1, CO2, CO3
6.	Verification of Half subtractor & Full subtractor circuit.	CO1, CO2, CO3
7.	Implementation of 4-Bit Parallel Adder Using 7483	CO1, CO2, CO3
8.	Verification of basic operation of a Multiplexer and Demultiplexer.	CO1, CO2, CO3
9.	Design and verify the 4-bit Synchronous counter.	CO1, CO2, CO3
10.	Design and verify the 4-bit Asynchronous counter.	CO1, CO2, CO3
11.	Familiarization of different types of flip-flops.	CO1, CO2, CO3

Text and Reference books:

Digital Logic and Computer Design by M. Morris Mano
Lab Manual

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery methods and activities	Assessment tools
CO1	PO1	C3, P2	Demonstration, Discussion, Experiment	Quiz/Written Exam, Performance, Report
CO2	PO2	C4, P3	Demonstration, Discussion, Experiment	Quiz/Written Exam, Performance, Report
CO3	PO10	A3	Demonstration,	Report

Course Title: Algorithm Design and Analysis

Course Code: CSE 225

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: DS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To practically implement basic algorithms, as well as to analyze the runtime and memory use.

Course Objectives:

The main objectives of this course are:

General Understanding of Basic Algorithms.
Developing Programming Skills for Solving Real Time Problem using Algorithm
Conduct experiments to get a sense of the time and storage (memory) efficiency of the program codes.
Demonstrate advantages and disadvantages of specific algorithms.
To make students learn how to use the measurement equipment for electronics system.
Test the physical characteristics of different component in lab.
Develop some practical circuits that describe the basic phenomenon of electronics.
Provide design concept of complex electronics system.
Introduce with the design issue of the electronic system.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1

Demonstrate (C2, P2) different electronic circuits and power supply as well as the

BJT amplifier/JFET amplifier, and their operation.

CO2

Conclude (C5) and show (P3) the result from experimental data.

CO3

Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

Course Description:

SL No.	Course Content	COs
1.	Familiarization with the components & devices used in Electronics Lab	CO1
2.	Find out the v-i characteristics of a semiconductor diode under forward & reverse biased condition	CO1, CO2, CO3
3.	Design a Half wave rectifier	CO1, CO2, CO3
4.	Design a Full wave rectifier using center taped transformer.	CO1, CO2, CO3
5.	Design a Full wave bridge rectifier.	CO1, CO2, CO3
6.	Find out the transistor characteristics curve for common emitter configuration.	CO1, CO2, CO3
7.	Find out the transistor characteristics curve for IB vs VBE.	CO1, CO2, CO3
8.	Find out the transistor characteristics curve for common base configuration.	CO1, CO2, CO3
9.	Single stage Common emitter configured NPN transistor amplifier.	CO1, CO2, CO3
10.	Observation of Common collector configured NPN transistor amplifier	CO1, CO2, CO3
11.	Observation of FET performance	CO1, CO2, CO3

Text Books, Reference Books and Other Resources:

Lab Manual

CO Delivery and Assessment:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2, P2	Lecture & Laboratory Experiments	Quiz, Performance Test, Report
CO2	PO4	C5, P3	Lecture & Laboratory Experiments	Performance Test, Report
CO3	PO10	A3	Demonstration	Report

Course Title: Object Oriented Programming

Course Code: CSE 211

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To empower the learner to perceive the fundamental knowledge of object-oriented programming paradigm and aimed at developing the skills of analyzing and solving real-world problems.

Course Objectives:

The objectives of this course are to:

Build an understanding of the object-oriented approach in programming.
Familiarize the student with the writing of computer programs to solve real-world problems in Java.
To make students learn and appreciate the importance and merits of proper comments in source code and API documentation.
Be able to write GUI interfaces for a computer program to interact with users, and to understand the event-based GUI handling principles.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Explain (C2) the fundamental OOP concepts (Classes, Operators, Variables, Keywords, Objects, Methods, Constructors, and Packages).
CO2	Illustrate (C3) different object-oriented principles (Encapsulation, Polymorphism, Abstraction and Inheritance) to solve engineering problems.
CO3	Demonstrate (C3) object-oriented features: exception handling, multi-threading, generics, collection framework, file handling, and GUI.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction: Basic concepts on object-oriented programming (OOP), benefits and application areas of Object-Oriented Programming, procedural vs. OOP programming, Comparison with OOP and other languages paradigm, and important features of OOP	CO1
2.	Class, objects, and constructor: Introducing classes, objects and constructors (class fundamentals, declaring objects, assigning object reference variables)	CO1
3.	Method basics and method overloading: Introducing Methods, this Keyword, Garbage Collection, the finalize () Method, Overloading Methods, using objects as parameters, returning objects, constructor, and constructor overloading.	CO1, CO2,
4.	Inheritance: Method overriding, Inheritance, different types of inheritance	CO2
5.	Miscellaneous class: Understanding Static, Introducing Super, Final, Nested, abstract, wrapper, and Inner class	CO1, CO2
6.	Interface: Basics of interface, class vs. interface, multiple inheritances, defining an interface, dynamic initialization	CO2
7.	Multithreaded Programming: Basics of thread, differences between multithreading and multitasking, the concept of multithreaded programming, and different ways to create a new thread.	CO3
8.	Multithreaded Programming: Implementing the Runnable interface, the life cycle of a thread, deadlock, synchronization, and set thread’s priority.	CO3
9.	Managing Errors and Exceptions: Explain different types of errors, Errors vs. Exceptions, and common Java exceptions with the cause of occurrence. Exception handling mechanism, finally blocks, illustrating the usage of throw and throws, user-defined exception.	CO3
10.	Generics: Basic concepts of generics in Java, advantages and use cases.	CO3
11.	Java Graphics: Enumerate the basic concepts of the Swing package.	CO3
12.	Event Handling and Layout Managers: AWT (Abstract Window Toolkit), Delegation Event Model, Event Listeners, and Sources of Events. Enumerate various types of event classes and listener interfaces. Adapter classes. Basics of the layout manager. Discuss different types of layout managers.	CO3
13.	File Handling (Input/Output): Stream in Java, the hierarchy of java stream classes, and purposes of the various stream functions. Stream Tokenizer, random and sequential access file, basic file-related programs to check the I/O. Basic file-related programs to check the I/O.	CO3
14.	Java Collections Framework: Java Map methods e.g., tree map, hash map. relevant programs. Java collections framework and iterator. relevant programs. List, set, and relevant programs.	CO3

Textbooks, Reference Books and Other Resources:

Programming with Java A Primer--- (E. Balagurusamy)
Java the Complete Reference --- (Herbert Schildt)
Java How to Program--- (Deitel and Deitel)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Object Oriented Programming Laboratory

Course Code: CSE 212

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SPL

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To empower the learner to perceive the fundamental knowledge of object-oriented programming paradigm and aimed at developing the skills of analyzing and solving real-world problems.

Course Objectives:

The objectives of this course are to:

Build an understanding of the object-oriented approach in programming.
Familiarize the student with the writing of computer programs to solve real-world problems in Java.
To make students learn and appreciate the importance and merits of proper comments in source code and API documentation.
Be able to write GUI interfaces for a computer program to interact with users, and to understand the event-based GUI handling principles.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C3) the fundamental OOP concepts (Classes, Objects and Packages) using Object Oriented Language.
CO2	Implement (C3) different object-oriented principles (Encapsulation, Polymorphism, Abstraction and Inheritance) to solve engineering problems.
CO3	Implement (C3) object-oriented features: exception handling, multi-threading, generics.
CO4	Implement (C3) GUI features to develop lightweight application based on constraints.
CO5	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

CO4

✓

CO5

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction: Basics of Java programming language, equation solving, conditional statement-related problem solving, and loop	CO1, CO5
2.	Class, objects, and constructor: Programs related to Class, Objects and Constructor	CO1, CO5
3.	Method overloading, Method Overriding, Inheritance, Abstraction, Polymorphism, Encapsulation	CO2, CO5
4.	Multithreaded Programming: Programs related to multithreading, creating a new thread, implementing the Runnable interface, synchronization, and setting the thread’s priority.	CO3, CO5
5.	Managing Errors and Exceptions: Programs related to errors and exceptions, common Java exceptions, exception handling mechanism, and finally block, illustrating the usage of throw and throws, user-defined exception.	CO3, CO5
6.	Applet: HTML and applet program integration, graphics programming with the applet, repaint () method, Swing package, Event Handling and Layout Managers	CO4, CO5

Text Books, Reference Books and Other Resources:

Programming with Java A Primer--- (E. Balagurusamy)
Java the Complete Reference --- (Herbert Schildt)
Java How to Program--- (Deitel and Deitel)

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO2	PO1, PO2	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO3	PO1	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO4	PO1, PO2	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO5	PO10	A3	Demonstration	Report

Course Title: Data Structures

Course Code: CSE 221

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Rationale:

Data structure provides a good understanding for organizing and storing data in a computer

such that it can be stored, retrieved, and updated frequently.

Course Objectives:

The main objectives of this course are to:

Make the students understand the data structures and their implementations.
Facilitate necessary knowledge about the importance of data structures in the context
of writing efficient programs.
Develop the ability to implement appropriate data structures in problem solving.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Interpret (C2) the basic concepts of data structures, their types and basic operations.
CO2	Summarize (C2) the strength and weaknesses of different data structures.
CO3	Use (C3) the appropriate data structures in the context of the solution to any given problem.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

Course Description:

SL No.	Course Content	COs
1.	Introduction to Data Structures, Basic Operations, and Performance Measurement.	CO1
2.	Introduction to Array, Search, and Sorting Strings: Pattern Matching Algorithm (Naive and KMP)	CO1, CO2, CO3
3.	Types of Linked List and Basic Operations on Linked List	CO1, CO2, CO3
4.	Basic Operations on Stack and Use of Recursion Types of Queue and Basic Operations on Queue	CO1, CO2, CO3
5.	Types of Trees, Representation of Tree, Binary Search Tree, Balanced Search Tree, Efficient Heap	CO1, CO2, CO3
6.	Graph Representation, Traversing Graph, Topological Sorting, Path Matrix, Warshall Algorithm	CO1, CO2, CO3
7.	Hash Function and its Application.	CO1, CO2

Text Books, Reference Books and Other Resources:

Seymour Lipschutz, Data Structures, McGraw-Hill, 2014.
Debasis Samanta, Classic Data Structures, Prentice-Hall of India Private Limited, 2009.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery methods and activities	Assessment tools
CO1	PO1	C2	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final
CO2	PO2	C2	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final Examination
CO3	PO1, PO2	C3	Lecture, Web link, PPT slide	Assignment, Class Test, Midterm examination, Final Examination

Course Title: Data Structures Laboratory

Course Code: CSE 222

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SPL

CIE Marks: 40 SEE Marks: 60

Rationale:

To practically implement as well analyze the various data structures and basic algorithm

analysis.

Course Objectives:

The main objectives of this course are to:

Help the students to understand the analysis and design of linear and non-linear data
structures in the context of writing efficient programs.
Develop the ability in identifying and applying the suitable data structure for the given

real- world problem.

Course Outcomes (COs):

CO1	Describe (C2) the operations of basic data structures.
CO2	Classify (C2) the appropriate data structure to solve specific problems.
CO3	Solve (C3) problems or improve existing code using learned data structures.
CO4	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

CO4

✓

Course Description:

SL

No.

Course Content

COs

1.

Identify and apply the basic operations of data structures: Array, Linked List, Stack, Queue, Tree, Graph.

CO1, CO4

2.

Identify the basic data structures and apply solutions: Bubble Sort, Selection Sort, Insertion Sort, Linear Search, Binary Search, Pattern matching: Naïve, KMP, Binary Search Tree, Heap Tree (Using Priority Queue), Breadth-first Search, Depth-first Search

CO2, CO3, CO4

Text Books, Reference Books and Other Resources:

Data Structures (Third Edition), by Seymour Lipschutz, Publishers: McGraw-Hill.
Classic Data Structures, by D. Samanta, Publishers: Prentic-Hall of India Private Limited.
Data Structures and Algorithms in Python, by Michael T. Goodrich, publisher: VP & PUBLISHER

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO2	PO2	C2	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO3	PO2	C3	Lecture, Practical Implementation	Lab Performance, Quiz, Report, Lab exam
CO4	PO10	A3	Demonstration	Report

Course Title: Digital Electronics

Course Code: EEE 311

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course aims to empower the learner to inspect and perceive the fundamental knowledge of digital electronics and develop sufficient background for advanced digital electronic courses.

Course Objectives:

The objectives of this course are to:

Provide a modern introduction to logic design and the basic building blocks used in technical areas in particular digital computers.
Familiarize the student with fundamental principles of 21st century’s digital design.
Provide coverage of classical hardware design for both combinational and sequential logic circuits.
Develop research skills and aspects of professional practice through group-bases assignments and team works that leads them to become successful self-employed, motivator, entrepreneur and manager.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) the fundamental concepts, theories and techniques used in digital electronics.
CO2	Interpret (C2) various combinational and sequential circuits.
CO3	Implement (C3) descriptions of logical problems to efficient digital logic circuits.
CO4	Demonstrate (C3) memory, interfacing and converter.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Number systems: Representation of numbers in different bases, Addition and subtraction in different bases, complement: Subtraction using complements, Binary multiplication & division.	CO1
2.	Binary codes: Different coding system, Boolean algebra, various gates, Sum of products and product of sums, Standard and canonical forms and other logical operations.	CO1
3.	Simplification of Boolean functions: Karnaugh map method, Tabular method of simplification; Implementation of logic circuit using various gates, Universal gates.	CO2
4.	Combinational logic circuit: Design procedure: Adder, Subtractor, Code converters, Parity bit checker and magnitude comparator, Analysis of different combinational circuits, Encoder, decoder, Multiplexer, Demultiplexer, ROM, PLA and their applications.	CO3
5.	Flip-flops: SR, JK, Master slave, T and D type flip-flops and their characteristic tables & equations; Triggering of flip-flops, Flipflop, Excitation table.	CO3
6.	Sequential circuits: Introduction to sequential circuits, Analysis and synthesis of synchronous and asynchronous sequential circuits.	CO3
7.	Counters: Classifications, Synchronous and asynchronous counter design and analysis, Ring counter, Johnson counters, Ripple counter and counter with parallel load.	CO3
8.	Registers: Classification, Shift registers, Circular registers and their applications and registers with parallel load. Basic Concept of Application Specific IC (ASIC) design.	CO3
9.	Digital IC logic families: Brief description of TTL, DTL, RTL, ECL, I2L, MOS and CMOS logic and their characteristics, principles of operation and application.	CO2
10.	Digital IC logic families: Brief description of TTL, DTL, RTL, ECL, I2L, MOS and CMOS logic and their characteristics, principles of operation and application.	CO2
11.	Memory Units: Various memory devices and their interfacing.	CO4
12.	Converters: Digital to Analog (D/A), Analog to Digital (A/D) converters, and their applications.	CO4

Text Books, Reference Books and Other Resources:

Digital Logic and Computer Design by M. Morris Mano
Modern Digital Electronics, by R.P. Jain.
Digital Electronics, by Tocci.
Computer Engineering by M. M. Mano,
Digital Computer Electronics by A.P. Malvino.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO2	PO1	C2	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO3	PO1, PO2	C3	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination
CO4	PO1, PO2	C3	Lectures, Notes, Practice Problems	Assignment, Class Test, Mid Term, Final Examination

Course Title: Digital Electronics Laboratory

Course Code: EEE 312

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This subject aims to get acquainted with integrated circuits, to design and implement digital circuits, and to be able to troubleshoot implemented circuits.

Course Objectives:

The objective of the course is to enable the students to:

Make students understand the operations and applications of various digital electronic circuits.
Help students develop the analyzing skills of digital electronic circuits.
Help students develop the ability to present the results of investigations orally and in writing.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C3, P2) different logic gate circuits as well as the multiplexer/demultiplexer and counter operation.
CO2	Conclude (C4) and show (P3) the result from experimental data.
CO3	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Familiarization of Digital Electronics Laboratory	CO1
2.	Familiarization of the Digital Logic gates ICs, truth tables & verification of Fundamental logic gates.	CO1, CO2, CO3
3.	Boolean function implementation.	CO1, CO2, CO3
4.	Verification of universality of NAND & NOR gates	CO1, CO2, CO3
5.	Verification of Half adder & Full adder circuit.	CO1, CO2, CO3
6.	Verification of Half subtractor & Full subtractor circuit.	CO1, CO2, CO3
7.	Implementation of 4-Bit Parallel Adder Using 7483	CO1, CO2, CO3
8.	Verification of basic operation of a Multiplexer and Demultiplexer.	CO1, CO2, CO3
9.	Design and verify the 4-bit Synchronous counter.	CO1, CO2, CO3
10.	Design and verify the 4-bit Asynchronous counter.	CO1, CO2, CO3
11.	Familiarization of different types of flip-flops.	CO1, CO2, CO3

Text and Reference books:

Digital Logic and Computer Design by M. Morris Mano
Lab Manual

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery methods and activities	Assessment tools
CO1	PO1	C3, P2	Demonstration, Discussion, Experiment	Quiz/Written Exam, Performance, Report
CO2	PO2	C4, P3	Demonstration, Discussion, Experiment	Quiz/Written Exam, Performance, Report
CO3	PO10	A3	Demonstration,	Report

Course Title: Algorithm Design and Analysis

Course Code: CSE 225

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: DS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

To practically implement basic algorithms, as well as to analyze the runtime and memory use.

Course Objectives:

The main objectives of this course are:

General Understanding of Basic Algorithms.
Developing Programming Skills for Solving Real Time Problem using Algorithm
Conduct experiments to get a sense of the time and storage (memory) efficiency of the program codes.
Demonstrate advantages and disadvantages of specific algorithms.

Course Outcomes:

Upon successful completion of the course the students will be able to:

CO1	Interpret (C2) the fundamental concepts and design paradigms of algorithms.
CO2	Calculate (C2) the time and space complexities of various algorithms.
CO3	Implement (C3) efficient algorithms in simple engineering design situations.

Mapping of Course Outcomes to Program Outcomes

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

Course Description:

SL No.	Course Content	COs
1.	Fundamental Algorithm Paradigms: Deterministic and nondeterministic algorithm, Divide and conquer algorithms, Dynamic Programming, Greedy algorithms, Minimum Spanning Tree, Single Source Shortest path algorithms, Incremental Improvement, Backtracking, Assignment Problem, Branch & Bound and Approximation Problem.	CO1
2.	Complexity Analysis and Evaluation: Space and time complexity, Order of growth, Recurrences relations, Amortization, Complexity classes.	CO2
3.	Real time applications and complexity comparison for engineering problems.	CO3

Recommended and Supplementary Books:

Introduction to Algorithms (Third Edition), by Coreman et, Publishers: McGraw-Hill
Fundamentals of Computer Algorithms, Sartaj Sahni and Sanguthevar Rajasekaran Ellis Horowitz

Mapping of Course Outcomes with Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Web Content, PPT slide	Class Test, Assignment, Midterm Exam, Final Examination
CO2	PO1, PO2	C2	Lecture, Web Content, PPT slide	Class Test, Assignment, Midterm Exam, Final Examination
CO3	PO1, PO2	C3	Lecture, Web Content, PPT slide	Class Test, Assignment, Midterm Exam, Final Examination

Course Title: Algorithm Design and Analysis Laboratory

Course Code: CSE 226

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite: DSL

CIE Marks: 40 SEE Marks: 60

Rationale:

This course introduces students to the general tools and techniques for analyzing and designing computer algorithms.

Course Objectives:

Upon completion of this course, students will be able to do the following:

Acquaint students with the major algorithms and data structures.
Foster the analytical and critical analysis of algorithmic design paradigms and methods.
Apply the knowledge of asymptotic performance of algorithms.
To facilitate necessary knowledge about algorithms in common engineering design

situations.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Use (C3) the appropriate algorithms to solve problems belonging to randomized, iterative, recursive, dynamic, greedy, graph, backtracking, branch and bound paradigms.
CO2	Argue (C5) the efficiency and correctness of the algorithms in CO1.
CO3	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.

Course Content

COs

1.

Analysis, Implementation and Evaluation of Divide & conquer algorithms: Merge sort, Quick sort, Dynamic Programming: 0/1 knapsack, Subset Problem; BFS, DFS, Topological Sorting, Minimum Spanning Tree :Prim’s Algorithm, Krushkal’s Algorithm, Single Source Shortest paths: Bellman-Ford, Dijkstra's algorithm; Backtracking: Hamiltonian Cycle, N Queen's problem, Branch & Bound : Travelling Salesman Problem

CO1, CO2,

CO3

Text Books, Reference Books and Other Resources:

Introduction to Algorithms (Third Edition), by Coreman et, Publishers: McGraw-Hill

2 Fundamentals of Computer Algorithms, Sartaj Sahni and Sanguthevar Rajasekaran Ellis Horowitz

Data Structures and Algorithms in Python, by Michael T. Goodrich, publisher: VP & PUBLISHER

Mapping of Course Outcomes with Teaching -Learning and Assessment Delivery and Assessment:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO2	C3	Lecture, Practical Implementation	Lab Performance, Quiz, Lab Exam
CO2	PO2	C5	Lecture, Practical Implementation	Lab Performance, Quiz, Lab Exam
CO3	PO10	A3	Demonstration	Report

Course Title: Database Management System

Course Code: CSE 237

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: ICS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course will cover the principles of relational database management systems, as well as recent advances in database theory and practice.

Course Objectives:

To make the students understand the different issues involved in the design and implementation of a database system in a modern information sys
To acquaint students with database modeling techniques/tools of the modern era.
To learn various techniques for managing a database-oriented syste

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C2) key concepts of database management system and its security.
CO2	Write (C3) database query to serve user requirements.
CO3	Plan (C6) to design various data models minimizing database anomalies.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Introduction: database systems, purposes, applications, database architecture, database administrator Relational Model: structure, database schema, keys, schema diagrams, relational query, relational algebra Distributed Database (DDB): centralized vs distributed database, homogenous and heterogenous DDB, major objective, storage technique, deadlock, ACID properties, data structure, data mining and warehousing, indexing and hashing	CO1
2.	Query Language: SQL, basic query structure, set operations, NULL values, aggregate functions, nested subqueries, database modification Intermediate SQL: join, views, transactions, integrity constraints, SQL data types, index in SQL, authorization, Advanced SQL: SQL from application program, function, procedure, trigger	CO2
3.	ER model: different attribute, mapping cardinalities, primary key for entity and relationship, strong and weak entity, redundant attribute, reducing ER diagram to relational schema Enhanced ER diagram: supertype and subtype, specialization, generalization, aggregation, various constraints, different ER notations, entity clustering ER diagram to relation: cardinality constraints to relational mapping, converting different types of attributes in relation, practice problems, Database Normalization: various anomalies, importance of normalization, functional dependency, partial dependency, transitive dependency, 1NF, 2NF, practice problems, 3NF, BCNF, practice problems.	CO3

Text Books, Reference Books and Other Resources:

Database System Concepts (7^th Edition) by Silbarchatz
Fundamentals of Database Systems (7^th Edition) by Elmasri
Database Management System (3^rd Edition) by Ramakrishnan
Modern Database Management (10^th Edition) by Jeffrey A. Hoffer

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture notes, PPT slides	Class Test, Midterm, Assignment, Final
CO2	PO2	C3	Lecture notes, PPT slides	Class Test, Midterm, Assignment, Final
CO3	PO2, PO3, PO4	C6	Lecture notes, PPT slides	Class Test, Midterm, Assignment, Final

Course Title: Database Management System Laboratory

Course Code: CSE 238

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: ICS

CIE Marks: 30 SEE Marks: 70

Course Rationale:

This course is designed to introduce the concept of real-world database design through the implementation of a database-driven real-time project.

Course Objectives:

To facilitate necessary knowledge about the design of a database based on various scenarios.
To develop a real-world database application.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Execute (C3) query language using database panel or application programming.
CO2	Model (C3) a database for a given problem considering different anomalies.
CO3	Develop (C6) and Report (A3) a database solution to an engineering problem as an individual or group project.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Familiarization with database management system, Basic, Intermediate and Advanced SQL queries, executing queries from application programming	CO1
2.	Modeling: ER and EER, ER to relation, relation to ER, normalization	CO2
3.	Project: Activities, report, presentation slides.	CO3

Text Books, Reference Books and Other Resources:

Database System Concepts (7^th Edition) by Silbarchatz
Fundamentals of Database Systems (7^th Edition) by Elmasri
Database Management System (3^rd Edition) by Ramakrishnan
Modern Database Management (10^th Edition) by Jeffrey A. Hoffer
Web references: w3schools.com, www.mysqltutorial.org

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level (C: Cognitive, P: Psychomotor A: Affective)	Delivery Methods and Activities	Assessment Tools
CO1	PO2, PO5	C3	Demonstration, PPT slides	Lab Performance, Assignment
CO2	PO2, PO3, PO4, PO5	C3	Demonstration, PPT slides	Lab Performance, Assignment
CO3	PO2, PO3, PO4, PO5, PO8, PO9, PO10, PO11, PO12	C6, A3	Demonstration, PPT slides	Project Demo and Report

Course Title: Signals & Systems

Course Code: EEE 201

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: EM II

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course aims to provide basic knowledge about various types of signals and systems along with different analysis techniques.

Course Objectives:

The objectives of this course are:

To help students conceptualize the relation between signals & systems.
To make the students understand classification of signals & properties of systems.
Accumulate basic ideas about analogous system and differential equations.
Facilitate students with necessary knowledge to conceptualize the properties of Fourier Series
Acquaint students with the basic theory of Fourier and Laplace transformation.
Develop skills to apply Fourier and Laplace transformation to solve real-time problems.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Apply (C3) Linearity, Causality, Time invariance, Memory, Stability and Inevitability of any system.
CO2	Analyze (C4) how signals transform from time domain to frequency domain & transform from frequency domain to time domain.
CO3	Apply(C3) convolution on signals.
CO4	Analyze(C4) how signals convert between mechanical system and electrical system.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1.	Introduction to Signals: Introduction to the Signals - classification, Basic operation on signals, Elementary signals. Representation of signals using impulse function.	CO1
2.	Introduction to Systems: Introduction to the Systems – classification. Properties of System. Identification of systems by analyzing properties.	CO1
3.	Analogues system: Analogues system and their solution, Linear mechanical elements, Element representation, D’Alembert’s Principle, F-V analogy, F-I analogy, Mechanical coupling Device, Electromechanical Systems.	CO4
4.	Linear differential equation-I: Overview of linear differential equation-I, Solution of higher order linear differential equation with constant coefficient.	CO1
5.	Linear differential equation-II: Overview of linear differential equation-II, Solution of higher order linear differential equation with undetermined coefficient.	CO1
6.	Lumped element electrical systems: Lumped element electrical systems using linear differential equation-I Lumped element electrical systems using linear differential equation-II Lumped element electrical systems using linear differential equation-III	CO4
7.	Fourier Series: Overview of Fourier Series. Fourier series- properties, Harmonic representation, System response, Frequency response of LTI systems.	CO2
8.	Fourier transformation: Fourier transformation- properties, System transfer function, System response and distortion-less systems. Fourier transformation- Applications of time and frequency domain analyses: solution of analog electrical and mechanical systems.	CO2
9.	Laplace transformation: Fourier transformation to Laplace transformation (LT), LT of important functions. LT: gate function, LT of periodic functions.	CO2
10	Application of Laplace Transformation: Application of Laplace Transformation, inverse transform, solution of system equations, system transfer function. Inverse Laplace Transform. LT: Impulse function, convolution integral, superposition integral.	CO2
11	Convolution of Signals: Convolution of Signals. Introduction to Random signal, Stationery, Ergodicity, Noise models. Correlation and power spectrum, Distribution and density functions.	CO3

Text Books, Reference Books and Other Resources:

Signals and Systems by Haykin
Linear System Analysis by D.K. Cheng.
Signal & Systems Analysis using Transform Methods and MATLAB by M.J. Roberts

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final
CO2	PO2	C4	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final
CO3	PO1	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final
CO4	PO2	C4	Lecture notes, PPT slides, problem solving, web content	Class Test, Assignment, Midterm, Final

Course Title: Signals and Systems Laboratory

Course Code: EEE 202

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite:

CIE Marks: 40 SEE Marks: 60

Course Rationale:

This course aims to develop students’ knowledge to implement different transform methods in MATLAB environment.

Course Objectives:

The objective of the course is to enable the students to

Familiarize students with the MATLAB environment
Introduce with plotting & sub-plotting concept.
Learn simulation of Fourier, Laplace & Z-transformation.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	· Evaluate (A3, P4, C6) different types of Signals in Matlab or CAD tools.
CO2	Respond (A2, P2) in writing comprehensive reports on the work done in laboratory in a group and orally present the findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

Text and Reference books:

Signals and Systems by Haykin.
Linear System Analysis by D.K. Cheng.
Signal & Systems Analysis using Transform Methods and MATLAB by M.J. Roberts
Lab Manual

Course Description:

SL

No

Course Content

CLOs

1.

Laboratory work based on theory course EEE 201: Signals and Systems

CO1 – CO2

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs

Corresponding POs

Bloom’s Taxonomy Domain/Level

(C: Cognitive, P: Psychomotor A: Affective)

Delivery Methods and Activities

Assessment Tools

CO1

PO5, PO12

A3, P4, C6

Lectures, Web

Lab Performance, Quiz/Viva

CO2

PO9, PO10, PO12

A2, P2

Discussion and Presentation

Report and Presentation

Course Title: Computational Methods for Engineering Problems

Course Code: CSE 301

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: EM III

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course is designed to make the understanding about computational concepts.

Course Objectives:

The objectives of this course are to:

Provide knowledge for solving various scientific and engineering problems.
Analyze and evaluate the accuracy of common numerical methods in its applications.
Develop mathematical modeling skills for experiments and research.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Implement (C3) techniques to minimize errors of numerical methods.
CO2	Use (C3) numerical techniques for solving linear and nonlinear complex optimization problems.
CO3	Implement (C3) complex systems of interpolation, curve fitting, and numerical differentiation and integration.
CO4	Implement (C3) numerical algorithms for solving ordinary and partial differential equations.
CO5	Apply (C3) numerical methods to solve problems in computational linear algebra, such as matrix operations and eigenvalue calculations.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

CO5

√

Course Description:

SL

No.

Course Content

COs

1.

Discuss Geometrical method to find real root of the equation, Bisection method, False position method, Fixed point iteration, Newton Raphson method, Solution of linear system, Difference table, Forward Difference table and Backward Difference table; Fundamental theorem of the difference Calculus, Interpolation with equal interval, Interpolation with unequal interval, Numerical Differentiation, Numerical Integration, Solution of Ordinary Differential Equations

CO1 – CO5

Text Books, Reference Books and Other Resources:

Numerical Analysis, by Vasistha
Numerical Analysis, by Richard L. Burden,
Numerical methods for scientific and engineering computation, by Mahinder Kumar Jain, R. K. Iyengar, Rajendra K. Jain
Advanced Engineering Mathematics by H.K. Das
Numerical Analysis, by Vasistha
Numerical Analysis by Richard L. Burden

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C3	Lecture, Notes, Problem solution	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam
CO4	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam
CO5	PO1, PO2	C3	Lectures, Notes, Practice Problems	Class Test, Assignment, Midterm, Final Exam

Course Title: Computational Methods for Engineering Problems Laboratory

Course Code: CSE 302

Credits: 0.75 Class Hours/Week: 1.5

Course Type: Core Pre-requisite: EM I, EM II, EM III

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This course equips students with the knowledge and skills to solve complex mathematical problems encountered in computer science using efficient and accurate numerical techniques.

Course Objectives:

The objectives of the course are:

To brings light the numerous uses of numerical methods in engineering.
To obtain error estimates for numerical derivative formulae.
To demonstrate implementation of numerical problems for engineering problems

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Apply (C3) numerical methods for solving engineering problems.
CO2	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

Course Description:

SL

No.

Course Content

COs

1.

Lab based on theory course of Computational Methods for Engineering Problems

CO1, CO2

Text Books, Reference Books and Other Resources:

Computer Networking: A Top-Down Approach---- (James F. Kurose, Keith W. Ross)-- Instructor contents and PPT slides: http://gaia.cs.umass.edu/kurose_ross/instructor.php
Cisco Packet Tracer Simulation
Additional contents: Cisco PowerPoint slides, pdf tutorials will be provided.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs

Corresponding POs

Bloom’s Taxonomy Domain/Level

(C: Cognitive, P: Psychomotor A: Affective)

Delivery Methods and Activities

Assessment Tools

CO1

PO1, PO2, PO5

C3

Lecture, Laboratory Experiments

Quiz, Performance, Assignment

CO2

PO10

A3

Lecture, Laboratory Experiments

Quiz, Performance, Assignment

Course Title: Software Engineering & Information System Design

Course Code: CSE 305

Credits: 4 Class Hours/Week: 4

Course Type: Core Pre-requisite: DMS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Software Engineering and Information Systems is a profile where the software development is studied in a systematic, controllable and efficient way.

Course Objectives:

The objectives of this course are:

To introduces the concept and method required for the construction of large software intensive system. It aims to develop a broad understanding of the discipline of software engineering.
To complement this with detail knowledge of technique for the analysis and design of complex software system. It aims to set these techniques in an appropriate engineering and management context.
To provide a brief account of associated professional and legal issues.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Explain (C2) different concepts of software engineering and information system design.
CO2	Describe (C2) conventional and agile software engineering process models.
CO3	Illustrate (C3) different modeling techniques to represent the working process of a software system.
CO4	Demonstrate (C3) different types of software testing strategies.
CO5	Illustrate (C3) various types of software project variables to estimate software project and maintaining project risks for quality improvement.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

CO5

√

Course Description:

SL No.	Course Content	COs
1.	Introduction Software Engineering Introduction to Software Engineering, Purpose, Applications, failure curves, software applications, legacy software, Umbrella Activities.	CO1
2.	Software Process Model: Process Flow, Process Models (RAD, Waterfall, Incremental, Spiral, Agile, Prototype, Concurrent), Advantages, Disadvantages, Framework Activities, Task set, Agile Development, Agility, Human Factors, Extreme Programming	CO1, CO2
3.	Unified Modeling Language: Notations and basic concepts, Use Case Diagram, Sequence Diagram, Activity Diagram	CO3
4.	Software Testing: Exhaustive Testing, Selective Testing, Good Test, Testing Principles, Black box Testing, White box Testing, Equivalence Partitioning, Boundary Value Analysis, Cause-Effect, Unit Testing, Integration Testing, System Testing Graphing, Testing, Error, Fault, Failure, Acceptance Testing	CO4
5.	Estimation and Software Risk: Introduction to software project variables, project estimation, risk management, software quality management	CO5
6.	Information System Design: Information System Design Life Cycle, Requirements engineering, Role and attributes of a system analyst, Feasibility Analysis, Data Flow Diagrams, Document Flow Diagrams, Process specification, Data Input Methods	CO1, CO3, CO5

Text Books, Reference Books and Other Resources:

Analysis and Design of Information Systems (2nd Edition) V. Rajaraman.
Software Engineering: A Practitioner’s Approach (8th Edition) R. S. Pressman.
Software Engineering (9^th Edition) Martin Shooman.
Software Engineering (9^th Edition) by Ian Sommerville

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Textbook, slides	Class test, Midterm, Assignment, Final
CO2	PO1, PO2	C2	Lecture, Textbook, slides	Class test, Midterm, Assignment, Final
CO3	PO2, PO3	C3	Lecture, Textbook, slides	Class test, Midterm, Assignment, Final
CO4	PO1	C3	Lecture, Textbook, slides	Class test, Midterm, Assignment, Final
CO5	PO1	C3	Lecture, Textbook, slides	Class test, Midterm, Assignment, Final

Course Title: Software Engineering & Information System Design Laboratory

Course Code: CSE 306

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: DMSL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

Software Engineering and Information Systems is a profile where the software development is studied in a systematic, controllable and efficient way.

Course Objectives:

The objectives of the course are:

To impart state-of-the-art knowledge on Software Engineering and UML in an interactive manner through the Web.
To present case studies to demonstrate practical applications of different concepts.
To provide a scope to students where they can solve small, real life problems.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Use (C3) appropriate tools for managing a software project.
CO2	Implement (C3) various client-side and server-side concepts utilizing design pattern to develop a software.
CO3	Investigate (C4) a system and Report (A2) the overall working process using modern tools.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Project Management: Project collaboration, Task Management	CO1
2.	Implementation: Client side and server-side feature implementation, design pattern and modular approach, software testing	CO2
3.	System Modeling: Project Proposal, Process model selection, UML diagram design, SRS document	CO3

Text and Reference Books:

Head First Design Patterns by Eric Freeman, Elisabeth Robson, Bert Bates, Kathy Sierra
Downloaded Software Engineering with UML by Bhuvan Unhelkar
Practical UI Patterns for Design Systems. Fast-Track Interaction Design for a Seamless User Experience by Diana MacDonald

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO5	C3	Lecture, slides	Class performance, Assignment, Report
CO2	PO1, PO2, PO3, PO5	C3	Lecture, slides	Class performance, Assignment, Report
CO3	PO2, PO4, PO5, PO9, PO10	C4, A2	Lecture, slides	Class performance, Assignment, Report

Course Title: Communication Engineering

Course Code: EEE 309

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This Course is offered to introduce the basic principles and applications of analog and digital

communication in our daily life.

Course Objectives:

The objectives of this course are:

To introduce students with the fundamental concepts of communication systems.
To provide the knowledge of different analog modulation schemes and digital modulation schemes.
To make the students understand the multiple access techniques and key aspects of noise.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Explain (C2) different types of analog modulation and digital modulation
CO2	Solve (C3) mathematical problems related to Communication engineering.
CO3	Analyze (C4) various channels and noises of communication system.
CO4	Analyze (C4) graphical and mathematical system of multiplexing & De-multiplexing.
CO5	Design (C6) Communication system

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction of communication systems: Basic principles, fundamental elements, system limitations.	CO2
2.	Information Theory: Information and system capacity, Information transmission, Entropy, Continuous channel capacity, Transmission through electrical network.	CO2
3.	Analog communication: AM, FM, PM, DSB, SSB, VSB, ISB.	CO1
4.	Digital communication: Introduction, Nyquist sampling theorem, Quantization of analog system, Quantization noise, PAM, PWM, PPM, PCM, LOGPCM, and systems, Digital modulations, ASK, FSK, PSK, DPSK, MSK, M-array digital modulation, QAM, QPSK, Delta modulation, Multi carrier modulation, line coding, Frame construction, Error Probability. Introduction to Radar and Satellite Communication. ISDN, B-ISDN, SONET, SDH modulations, ASK, FSK, PSK, DPSK, MSK, M-array digital modulation, QAM, QPSK, Delta modulation, Multi carrier modulation, line coding, Frame construction, Error Probability. Introduction to Radar and Satellite Communication. ISDN, B-ISDN, SONET, SDH	CO1, CO5
5.	Multiplexing: Space division multiplexing, frequency division multiplexing, time division multiplexing, and code division multiplexing.	CO4, CO5
6.	Noise: Physical sources of noise, types of noise, calculation of noise, SNR & noise figure, and calculation of noise figure, noise temperature, equivalent noise resistance.	CO3

Text Books, Reference Books and Other Resources:

Electronic Communication System by George Kennedy.
Modern Digital & Analog Communication Systems by B.P. Lathi.
Communication System by Simon Haykin.
Principles of Communication Systems by Herbert Taub and Donald L. Schilling

CO Delivery and Assessment:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, PPT slide, Mathematical Problem solving	Class Test, Assignment, Mid Term, Final Exam
CO2	PO1, PO2	C3	Lecture, PPT slide, Mathematical Problem solving	Class Test, Assignment, Mid Term, Final Exam
CO3	PO2	C4	Lecture, PPT slide, Mathematical Problem solving	Class Test, Assignment, Mid Term, Final Exam
CO4	PO2	C4	Lecture, PPT slide, Mathematical Problem solving	Class Test, Assignment, Mid Term, Final Exam
CO5	PO2	C6	Lecture, PPT slide, Mathematical Problem solving	Class Test, Assignment, Mid Term, Final Exam

Course Title: Communication Engineering Laboratory

Course Code: CSE 310

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 40 SEE Marks: 60

Course Rationale:

This course aims to introduce students with basic techniques and tools for electrical circuit simulation.

Course Objectives:

The objective of the course is to enable the students to

To develop knowledge of using simulation tools for simulating electric circuit.
To distinguish the difference between the simulation behavior and behavior of practical circuit.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C5, A4, P3) different types of analog and digital modulation and demodulation techniques using experimental setup as an individual or as a member of a team.
CO2	Conclude (C6, A3, P1) the result from experimental data.
CO3	Interpret (A4, P2) the laboratory work done in a group to present the findings.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Laboratory work using hardware based on theory course: EEE 309: Communication Engineering. It will cover AM FM, ASK, FSK, PSK modulation and demodulation practical laboratory work.	CO1, CO2, CO3
2.	Report writing based on laboratory work.	CO3
3.	Oral presentation on mini project work (design project/analytical project/ experimental project/industrial tour).	CO1, CO2

Text and Reference books:

Lab Manuals
Additional contents: Communication Engineering PowerPoint slides, pdf tutorials will be provided.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO5, PO9, PO10	C5, A4, P3	Lecture, Demonstration, discussion, experiment	Quiz/Written Exam, Performance, Report
CO2	PO4, PO12	C6, A3, P1	Lecture, Calculation, Graph Drawing, Report	Class Performance, Report
CO3	PO9, PO10, PO12	A4, P2	Lecture, Mini Project, Presentation, Report Writing	Class Performance, Quiz/Written Exam, Presentation

Course Title: Microprocessors and Microcontrollers

Course Code: EEE 371

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: DE

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Intended to enable the learners to familiar with different types of microprocessors and microcontrollers, use the acquired knowledge to understand computer systems and embedded systems circuitry or architectures.

Course Objectives:

The objectives of this course are to:

Provide a theoretical &empirical introduction to microcontrollers and microprocessors, assembly language programming techniques, design of hardware interfacing circuit, microcontroller, and microprocessor system design considerations.
Provide the rudimentary concepts to identify the basic elements and functions of contemporary microprocessors and microcontrollers and implicating programming in the instruction sets of microprocessors and microcontrollers (Basically Intel family).
Designing and maintaining different types of digital projects to fulfill the quotidian life problem in embedded system.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Describe (C2) the basics of microprocessor and microcontroller architecture, interfacing, and operations.
CO2	Execute (C3) assembly language instructions or programs to control microprocessor and microcontroller operations.
CO3	Solve (C3) the real-world problems related with embedded systems and designs.
CO4	Illustrate (C2) microprocessor and microcontroller related circuitry and sensor interfacing.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to different type of microprocessors: 8 bit, 16 bit, 32 bit and their architectures; pin diagram & junction; Intel series microprocessor and Co-processor; RISK & CISK processor; Assembly Language: Basic Instruction Sets and Assembly language Programming based on 8086 microprocessor.	CO1
2.	Microprocessor peripherals: Introduction to some available microprocessor peripherals IC's and their application such as 8251, 8253, 8254, 8255, 8257, 8259, 8279, A/D and D/A converter interfacing, Timing Diagram, Interrupts, I/O systems, DMA-based data transfer, memory interfacing. MMX and SIMD technologies. The above peripheral is based on 8085 and 8086 processor.	CO2
3.	Interfacing: Introduction, interfacing to microprocessor to keyboards, alphanumeric displays. Introduction to microcomputers and interfacing to microcomputer ports to high power devices.	CO3
4.	Basic Micro controller: Introduction of microcontroller; embedded system design; microcontroller programming environment; Architecture of different microcontroller such as PIC, MSP, ARM etc. Real time application design based on microcontroller.	CO3, CO4
5.	PIC Microcontroller: Introduction to PIC Microcontroller, Internal Architecture, Components & Programming. Some PIC Based Project work.	CO3, CO4
6.	Arduino & Raspberry Pi: Introduction of Development boards.	CO3, CO4

Text Books, Reference Books and Other Resources:

The Intel Microprocessor Architecture, Programming and Interfacing, Barry B. Brey.
Microprocessors and Interfacing: Programming and Hardware,Douglas V. Hall.
Advanced Microprocessor and Interfacing, Badri Ram.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment tools
CO1	PO1	C2	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO2	PO1	C3	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO3	PO3	C3	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO4	PO1	C2	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam

Course Title: Microprocessors and Microcontrollers Laboratory

Course Code: EEE 372

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: DEL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

The course is intended to introduce the basic concepts of microprocessor and to develop in students the assembly language programming skills and real time applications of Microprocessor as well as microcontroller.

Course Objectives:

The objectives of this course are to:

Learn about different types of microcontroller & Microprocessors
Gather knowledge about different types of sensors used in our day-to-day life.
Learn about microcontroller programming.
Design simple project based on microcontroller

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Describe (P1) and write program in assembly language for 8086 microprocessors.
CO2	Design (C3, P3) micro-controller based Real Time Projects
CO3	Design (C3, P3) SBC based system.
CO4	Perform work (C6, A3, P3) in individual and or group project.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Study on micro controller and tinkercad simulator	CO1
2.	Familiarizing with Arduino, Arduino IDE & design a simple LED looping Circuit.	CO2
3.	Interfacing with stepper motor.	CO2
4.	Keyboard & LCD display interfacing	CO2
5.	PWM & its use in Arduino using Servo Motor.	CO2
6.	Interfacing with different types of sensors with Arduino module	CO2
7	Interfacing with Bluetooth module	CO2
8	Introduction to Single board computer (SBC)	CO3, CO4
9	Project Work	CO3, CO4

Text Books, Reference Books and Other Resources:

Lab manual

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	P1	Lecture, Slide, Problem Solving, and examples	Quiz, Performance Test
CO2	PO3, PO5	C3, P3	Lecture, Slide, Problem Solving, and examples	Quiz, Performance Test
CO3	PO3, PO5	C3, P3	Lecture, Slide, Problem Solving, and examples	Quiz, Performance Test
CO4	PO3, PO5, PO9, PO10	C6, A3, P3	Lecture, Slide, Problem Solving, and examples	Project submission, Presentation

Course Title: Artificial Intelligence

Course Code: CSE 317

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course introduces the basic concepts and techniques of Artificial Intelligence (AI) to creating software and hardware to get computers to do things that would be considered intelligent as if people did them.

Course Objectives:

Introducing the students to AI’s fundamental problems and state-of-the-art algorithms.
Appreciating the role of knowledge representation, logic, and language in understanding human intelligence from a computational perspective.
Helping the students conceptualize the far-reaching goal of AI in shaping our future.
Exposing the students to the frontiers of AI-intensive computing while providing a sufficiently strong foundation to encourage further research.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Interpret (C2) the key components and classical search algorithms of AI to solve various real-life problems.
CO2	Investigate (C4) statistical methods and machine learning techniques for solving complex AI-related problems
CO3	Use (C3) Natural Language Processing methods for real-life problem solving.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to Artificial Intelligence AI Subfields, AI agent, Task Environment, Environment Type	CO1
2.	Searching Techniques: BFS, DFS, UCS, Best First Search, A*, Genetic Algorithm	CO1
3.	Introduction to Natural Language Processing (NLP) Introduction, Intuitions, Application Fields, Text Representation, Different Language Models (char/word n-grams), Text Similarity Measure, Machine Translation.	CO3
4.	Introduction to Uncertainty Quantifying Uncertainty, Basic Probability Notation, Inference using full joint distributions, Bayes Rule, Semantics of Bayesian network (BN), Inference in BN, Probabilistic Reasoning over time, Hidden Markov Models.	CO2
5.	Introduction to Learning Nearest Neighbors, Linear Regression, Logistic Regression, Decision Tree, K Means Clustering, Naïve Bayes Classifier, Reinforcement Learning	CO2

Text Books, Reference Books and Other Resources:

"Artificial Intelligence – A Modern Approach", by Stuart Russell and Peter Norvig.
"Speech and Language Processing", Jurafsky & Martin, Third Edition (Draft Link)
"Artificial Intelligence",by Elaine Rich, Kevin Knight.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO2	C4	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO2, PO5	C3	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam

Course Title: Artificial Intelligence Laboratory

Course Code: CSE 318

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SPL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

This subject aims to practically implement as well as analyze the various procedures, methods, and algorithms related to artificial intelligence for real-world applications.

Course Objectives:

The objective of the course is to enable the students to

Provide students with a practical aspect of various AI algorithms
Motivate them towards developing their careers in AI applications

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	· Demonstrate (C2) the application of modern tools for data preprocessing and analysis.
CO2	Apply (C3) search strategies for solutions to problems with complete information.
CO3	· Develop (C3) an intelligent system capable of learning from data.
CO4	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to Python Basic Syntax, Functions, Machine Learning Tools	CO1
2.	Search Techniques Implementation: Uninformed Search Techniques, Informed Search Techniques	CO2
3.	Project Implementation Text Data Reading and Preprocessing, Text Representation, Model Implementation	CO3

Text and Reference books:

"Artificial Intelligence – A Modern Approach", by Stuart Russell and Peter Norvig

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2, PO5	C2	Lecture, Lab Task	Lab Performance, Assignment
CO2	PO1, PO2, PO5	C3	Lecture, Lab Task	Lab Performance, Assignment
CO3	PO1, PO2, PO3, PO9	C3	Lecture, Web links	Project Report, Performance, Presentation, Viva
CO4	PO10	A3	Demonstration	Report

Course Title: Operating Systems

Course Code: CSE 333

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course provides a comprehensive understanding of the modern Operating System and examines the ways that design goals can be achieved.

Course Objectives:

The objectives of this course are:

To enhance students' understanding of the function and structure of operating systems
To facilitate necessary knowledge about how the system interacts and provides services for the execution of application software.
To evaluate underlying hardware, virtual memory, concurrency, and synchronization.
To enable them to illustrate key operating system aspects by concrete examples, and prepare them for future courses.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) the basic structure and characteristics of a modern operating system, the concept for virtualization, cloud, and multiple processor systems.
CO2	Explain (C2) the different process and thread synchronization methods and the tradeoffs between them.
CO3	Assess (C4) the algorithms on which the functions of the Operating Systems are built.
CO4	Compare (C3) different types of memory-management schemes for a system.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction: Operating system, Computer-system organization, and architecture, Operations, Computing environments.	CO1
2.	Operating-System Structures: Operating system services, System calls, Operating system structure.	CO1
3.	Processes: Process concept, Process scheduling, Operations on processes, Communication	CO2
4.	Multithreaded Programming: Threading overview, Multithreading models	CO2
5.	Process Scheduling: Basic concept, Criteria, Algorithms	CO3
6.	Deadlocks: Characterization, Methods for handling deadlocks, Deadlock Prevention, Avoidance, Detection.	CO3
7.	Main Memory: Background, Swapping, Segmentation, Paging	CO4
8.	Virtual Memory: Background, Demand Paging, Page replacement	CO4
9.	File System Interface: File Concept, Access methods, Directory structure	CO1

3

Text Books, Reference Books, and Other Resources:

Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, “Operating System
Concepts”, (8^th Edition) ISBN: 978-0-470-23399-3.
Andrew S. Tanenbaum (Author), Herbert Bos, “Modern Operating Systems”

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Problem solution	Assignment, Class Test, Midterm, Final
CO2	PO1, PO2	C2	Lecture, Problem solution	Assignment, Class Test, Midterm, Final
CO3	PO1, PO2	C4	Lecture, Problem solution	Assignment, Class Test, Midterm, Final
CO4	PO1, PO2	C3	Lecture, Problem solution	Assignment, Class Test, Midterm, Final

Course Title: Operating Systems Laboratory

Course Code: CSE 334

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 40 SEE Marks: 60

Course Rationale:

This course provides a comprehensive understanding of the modern Operating System and examines the ways that design goals can be achieved.

Course Objectives:

The objectives of this course are to:

Learn and use the shell commands of Operating Systems.
Familiarize students with the architecture of an OS.
Provide necessary skills for developing and debugging programs in different environments.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Interpret (C2) directory operation using GUI & Terminal.
CO2	Use (C3) different shell commands for basic system operation.
CO3	Configure (C3) different servers (such as Web, DNS, FTP, Email, etc.) through the knowledge gained in this course.
CO4	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to various Operating Systems	CO1, CO4
2.	Basic Overview of Boot Process	CO1, CO4
3.	User management and Basic Shell commands	CO1, CO4
4.	Essential Commands	CO1, CO4
5.	Advanced commands & tools for System Administration	CO1, CO4
6.	Shell script and Scheduling	CO1, CO4
7.	Configuring a basic server	CO1, CO4

Text Books, Reference Books, and Other Resources:

Red Hat Enterprise Linux 6 Administration By Sander Van Vugt.
https://linuxconfig.org

https://www.linuxfoundation.org

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Demonstration, discussion, experiment	Assignment, Performance
CO2	PO1	C3	Demonstration, discussion, experiment	Assignment, Performance, Report, Quiz
CO3	PO3	C3	Demonstration, discussion, experiment	Assignment, Performance, Lab Exam
CO4	PO10	A3	Demonstration	Report

Course Title: Computer Organization & Architecture

Course Code: CSE 337

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Intended to enable the learners to gather proper knowledge in hardware design by

understanding the fundamentals of core and modern computer architectures and hardware.

Course Objective:

The objectives of this course are:

To understand the basic structure and operation of a digital computer.
To familiarize students with hardware design and the operation of the arithmetic unit including the algorithms.
To learn different ways to communicate with I/O devices and standard I/O interfaces.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to-

CO1	Describe (C2) the basics of computer hardware and how software interacts with computer hardware.
CO2	Demonstrate (C2) computer performance using various assessing methods.
CO3	Explain (C2) how computers represent and manipulate data.
CO4	Assemble (C3) a simple computer with hardware design including data format, instruction format, instruction set, addressing modes, bus structure, input/output, memory, arithmetic/logic unit, control unit, and data, instruction and address flow.

Mapping of Course Outcomes to Program Outcomes:

	PO1	PO2	PO3	PO4	PO5	PO6	PO7	PO8	PO9	PO10	PO11	PO12
CO1	√
CO2	√
CO3	√
CO4	√	√	√

Course Description:

SL No.	Course Content	COs
1.	Instructions: Language of the computer, introduction, Operations of the Computer Hardware, Operands of the Computer Hardware, Representing Instructions in Computer, Logical Operations.	CO1
2.	Assessing and Understanding Performance: CPU Performance and its Factors, Evaluating Performance, Fallacies and Pitfalls.	CO1
3.	Basic Computer Organization & Design: Instruction Codes, Direct/Indirect Address, Computer Registers and Bus systems	CO2
4.	Design of a Basic Computer: Computer Instruction, Timing and Control, Instruction Cycle, Interrupts	CO2
5.	Enhancing Performance with Pipelining: An overview of Pipelining, A Pipelined Datapath, Pipelined Control, Data Hazards and forwarding, Branch Hazards, Fallacies and Pitfalls	CO3
6.	Central Processing Unit: General Register Organization, Stack Organization, Instruction Formats, Reverse polish notation, Instruction Formats, Address Instructions, Addressing Modes, Data Transfer and Manipulations.	CO3
7.	Computer Arithmetic: Multiplication Algorithm, Division Algorithm, Floating-Point Arithmetic Operation.	CO3
8.	I/O Organization: I/O Interface, Asynchronous Data Transfer, Strobe Control Handshaking, Asynchronous Serial Transfer, Asynchronous Communication Interface, DMA, DMA Controller, DMA Transfer in a computer system	CO4
9.	Memory Organization: Memory Hierarchy, Main Memory, Auxiliary Memory, Cache Memory, Memory Mapping	CO4
10.	Multiprocessors: Characteristics of multiprocessors, Interconnection Structures, Cache Coherence	CO4

Text Books, Reference Books, and Other Resources:

Morris Mano, “Computer System Architecture”, 3^rd Edition, Prentice-Hall India, 2003, ISBN-81-203-0855-7

David A. Patterson, “Computer Organization and Design”, 3^rd Edition.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level (C: Cognitive, P: Psychomotor A: Affective)	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Slide Presentation	Class Test, Assignment, Midterm, Final Exam
CO2	PO1	C2	Lecture, Slide Presentation	Class Test, Assignment, Midterm, Final Exam
CO3	PO1	C2	Lecture, Slide Presentation	Class Test, Assignment, Midterm, Final Exam
CO4	PO1, PO2, PO3	C3	Lecture, Slide Presentation	Class Test, Assignment, Midterm, Final Exam

Course Title: Software Development

Course Code: CSE 338

Credits: 2 Class Hours/Week: 4

Course Type: Core Pre-requisite: DMS

CIE Marks: 30 SEE Marks: 70

Course Rationale:

In the Software Development course, the students will learn how to build software from scratch. The program focuses on developing responsive designs, validation and testing, analyzing different methods for solving a particular problem, working in a group for continuous delivery etc. Students will learn about the latest project management tools, how to model a problem from various domains.

Course Objectives:

The objectives of this course are:

To contribute effectively to the development process of a c
To make students eligible to deal with changes during the middle of the development of software.
To prepare students to adapt to new technologies in the future.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	· Implement (C3) various server-side and client-side concepts using latest framework.
CO2	Use (C3) 3^rd party packages for developing software.
CO3	· Work (A4) in an individual or group project maintaining engineering ethics.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction: Familiarization to development framework, Client-side technologies, Server-side technologies, Report Writing with LATEX, Project Requirements	CO1, CO2, CO3
2.	Server-side and Client-side concepts: Design pattern, Routing, Interaction with database, CRUD operation, Authentication and Authorization, Validation, File Uploading, Client-Server Communication, API integration, Responsive Designs, 3^rd Party package Integration	CO1, CO2
3.	Project: Requirements analysis, identifying scopes, modeling the specifications, implementation, and representing all the steps via report	CO3

Text Books, Reference Books, and Other Resources:

World-wide web, YouTube Tutorials

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2, PO5	C3	Demonstration, experiment	Performance, Assignment
CO2	PO1, PO2, PO5	C3	Demonstration, experiment	Performance, Assignment
CO3	PO1, PO2, PO3, PO4, PO5, PO6, PO7 PO8, PO9, PO10, PO11, PO12	A4	Project Related Instruction	Project, Report, Presentation

Course Title: Data Communication

Course Code: CSE 364

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite:

CIE Marks: 60 SEE- Marks: 40

Course Rationale:

Data communication, which is the transmission of digital data through a network or to a device external to the sending device, is the cornerstone of modern telecommunications. Given the importance of different communication systems, this course is designed for Computer Science and Engineering students.

Course Objectives:

Enable the students to understand the fundamental concepts and techniques of Data Communication.
Equip the skill required to apply encoding and communication protocols.
Facilitate students to analyze the error detection and correction techniques.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Discuss (C2) the concepts of data communication systems and their components.
CO2	Explain (C2) the digital and analogue representations and channels.
CO3	Demonstrate (C3) the mechanism and techniques of encoding.
CO4	Describe (C2) the general principles of communication protocol switching techniques
CO5	Implement (C3) different error detection, correction and flow control techniques.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction: Data Communications, components, Data Representation, Data Flow	CO1
2.	Data and Signals: Analog and Digital, Periodic Analog signals, Digital Signals, Time and Frequency Domain, Composite Signal. Bandwidth, Digital Signal, Transmission of digital signal, Transmission Impairment, Noise, SNR. Data Rate Limits, capacity Theorem, Nyquist Bit rate, Shannon Bit rate. Performance, bandwidth Delay Product.	CO2
3.	Modulation and demodulation: amplitude modulation, frequency and phase Modulation Analog Transmission: Digital-to-Analog Conversion, Constellation Diagram	CO3
4.	Modulation and demodulation: amplitude modulation, frequency and phase Modulation Analog Transmission: Digital-to- Analog Conversion, Constellation Diagram	CO3
5.	Bandwidth Utilization: Multiplexing, Analog Hierarchy, Interleaving, Data Rate Management, Digital Hierarchy STDM, Spread Spectrum	CO2
6.	Error Detection and Correction: Introduction, Single Bit Error, Burst Error, Detection Vs Correction, Forward Error correction Vs retransmission, Block Coding Hamming Distance, Linear Block Codes, Flow control techniques.	CO5
7.	Circuit and Packet Switching techniques, different communication protocols	CO4

Text and Reference books:

Data Communication and Networking by Behrouz A Forouzan
Data and Computer Communications (9^th Edition), by William Stallings, Publishers: Prentice Hall

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level (C: Cognitive, P: Psychomotor A: Affective)	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lectures/Instructions Reading Materials/PPT slides	Class Test, Midterm, Assignment, Final
CO2	PO1	C2	Lectures/Instructions Reading Materials/PPT slides	Class Test, Midterm, Assignment, Final
CO3	PO1	C3	Lectures/Instructions Reading Materials/PPT slides	Class Test, Midterm, Assignment, Final
CO4	PO1	C2	Lectures/Instructions Reading Materials/PPT slides	Class Test, Midterm, Assignment, Final
CO5	PO1	C3	Lectures/Instructions Reading Materials/PPT slides	Class Test, Midterm, Assignment, Final

Course Title: Computer Networks

Course Code: CSE 367

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: ICS

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course aims to introduce the basic concept and essential knowledge of computer networks.

Course Objectives:

The objectives of this course are:

Building an understanding of the fundamental concepts of computer networking.
Familiarizing the student with the basic taxonomy and terminology of the computer networking area.
Introducing the students to advanced networking concepts, preparing the student for getting entry into advanced courses in computer networking.
Allowing the students to gain expertise in some specific areas of networking such as the design and maintenance of individual networks.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Interpret (C2) the OSI and TCP/IP layered architecture, different TCP/IP layer protocols, network organization, the operations of the key networking components, advance network architecture: software defined networking, 4G/5G, etc.
CO2	Describe (C2) the functionalities and performance of different routing and transport layer protocols, flow and congestion control mechanisms, link layer protocols, error detection and correction mechanisms, OpenFlow protocols.
CO3	Practice (C3) the acquired knowledge to configure/design wired/wireless networks and sub-networks, routing protocols, security principles to networking, solve different network issues, etc.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Computer Networks and the Internet: Operations: protocol layers, service models, Performance Metrices: delay, loss, throughput	CO1
2.	Application Layer: Principles of network applications, Web and HTTP, electronic mail SMTP, POP3, IMAP, DNS, socket programming with UDP and TCP	CO1, CO2
3.	Transport Layer: Transport-layer services, 3.2 multiplexing and demultiplexing, connectionless transport: UDP function, The TCP Connection, TCP Segment Structure, Round-Trip Time Estimation and Timeout, Reliable Data Transfer, Flow Control, TCP Connection Management, TCP Congestion Control: Classic TCP Congestion Control Slow start, congestion avoidance, fast recovery 3.7.1 Classic TCP Congestion Control Slow start, congestion avoidance, fast recovery 3.7 TCP Congestion Control 3.7.1 Classic TCP Congestion Control Slow start, congestion avoidance, fast recovery Week 6 Problem Solving Week 6 Concept Checking Wireshark Lab TCP Project 1 Web Server submission Week 6 Lab Report - TCP Project A Review Quiz 3 6 3.5.5 Flow Control 3.5.6 TCP Connection Management 3.7 TCP Congestion Control 3.7.1 Classic TCP Congestion Control Slow start, congestion avoidance, fast Recovery	CO1, CO2, CO3
4.	Network Layer: Data Plane Overview of Network layer, data plane, control plane, What’s inside a router? IP: Internet Protocol, datagram format, Fragmentation, IPv4 addressing, network address translation, IPv6 , Components of SDN Controller, OpenFlow protocol, Traffic engineering, difficult with traditional routing	CO2, CO3
5.	Network Layer: Control Plane Introduction, routing protocols, link state, distance vector, intra-AS routing in the Internet: OSPF, routing among the ISPs: BGP	CO2, CO3
6.	The Link Layer and LANs Link Layer introduction, services, The Services Provided by the Link Layer, Where Is the Link Layer Implemented? Error-Detection and -Correction Techniques: Parity Checks, Multiple Access Links and Protocols: Channel Partitioning Protocols, Random Access Protocols, Taking-Turns Protocols, Switched Local Area Networks: Link-Layer Addressing and ARP, MAC Addresses, Address Resolution Protocol (ARP), Sending a Datagram off the Subnet, Ethernet, Ethernet Frame Structure, Ethernet Technologies, Link-Layer Switches: Forwarding and Filtering, Self-Learning, Properties of Link-Layer Switching, Switches Versus Routers, VLAN, Port based VLANs, VLANs spanning multiple switches: Trunk link	CO1, CO2, CO3
7.	Wireless and Mobile Networks: Elements of a wireless network, Wireless network taxonomy, Wireless link characteristics, Hidden Terminal Problem and signal attenuation, CDMA, 802.11, IEEE 802.11 MAC Protocol, Personal area networks, Network Security: Possible attacks on networks and possible ways to fight with those attacks	CO1, CO2

Text Books, Reference Books and Other Resources:

Computer Networking: A Top-Down Approach---- (James F. Kurose, Keith W. Ross)
Instructor contents and PPT slides: http://gaia.cs.umass.edu/kurose_ross/instructor.php
TCP/IP illustrated. --- (Richard Stevens)
Data Communications and Networking: The Basic Toolbox--- (Behrouz A. Forouzan)
Computer Networks--- (Andrew S. Tanenbaum and David J)
Additional contents: Cisco PowerPoint slides, pdf tutorials will be provided.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Computer Networks Laboratory

Course Code: CSE 368

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: ICS

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This subject aims to teach an understanding of computer networks and systems design through hands-on lab works and analysis with real world applications.

Course Objectives:

The objective of the course is to enable the students to

Understand and implement the functionalities of major networking components, network topologies, and connections.
Become familiar with configuration of networking and routing protocols, network operations, state-of-the-art developments and applications.
Administer advanced topics such as WLAN, network security, threats mitigation, security best practices, network management, and/or other topics.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	· Identify (C1) basic networking and end devices: real equipment and simulation, install a home or small business network, connection to the Internet.
CO2	Illustrate (C2) different application layer protocols, distance vector and link-state routing protocols, and transport layer protocols
CO3	· Enhance (C2, A3) network security using access control lists and security best practices.
CO4	· Use (C3) the acquired knowledge to establish, verify and troubleshoot network and Internet connectivity.
CO5`	· Complete (P3) different types of network cables and device connections by hands-on.
CO6	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to network simulation tools, Introduction to different types of network cables	CO1, CO5, CO6
2.	Configure Application layer services	CO2, CO6
3.	Basic Router and Switch Configuration, Routing protocol configuration, Network Address translation	CO2, CO4, CO6
4.	Subnetting, VLSM, and IPv6	CO2, CO4, CO6
5.	Access control lists	CO3, CO6
6.	Wireless router configuration	CO3, CO4, CO6
7.	Implement a custom topology	CO4, CO6

Text Books, Reference Books and Other Resources:

Computer Networking: A Top-Down Approach---- (James F. Kurose, Keith W. Ross)
-- Instructor contents and PPT slides: http://gaia.cs.umass.edu/kurose_ross/instructor.php
Cisco Packet Tracer Simulation
Additional contents: Cisco PowerPoint slides, pdf tutorials will be provided.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C1	Demonstration, discussion, experiment	Quiz/Written Exam, Performance
CO2	PO1, PO2	C2	Demonstration, discussion, experiment	Quiz/Written Exam, Performance
CO3	PO2, PO5, PO8	C2, A3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance
CO4	PO2, PO5	C3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance
CO5	PO1, PO5	P3	Demonstration, discussion, experiment	Quiz/Written Exam, Performance
CO6	PO10	A3	Demonstration	Report

Course Title: Theory of Computation

Course Code: CSE 309

Credits: 2 Class Hours/Week: 2

Course Type: Core Pre-requisite: DM

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Theory of computation lays a strong foundation for abstract areas in computer science. Theory of computation teaches the learners about the elementary ways in which a computer can be made to think, how efficiently problems can be solved on a model of computation, using an algorithm, tends to set the foundation for understanding Compilers and interpreters.

Course Objectives:

The objectives of this course are:

To identify with the fundamental limits on what can be efficiently computed in our universe and other possible universes.
To analyze limits to reveal deep and mysterious properties about information, knowledge and processing, as well as practical issues about what can and cannot be computed.
To design & build models for complex computation.

Course Outcomes (COs):

After successful completion of this course, students will be able to:

CO1	Comprehend (C2) formal reasoning about languages.
CO2	Categorize (C4) types of different computing languages.
CO3	Investigate (C4) advanced knowledge of formal computation and its relationship to languages.
CO4	Illustrate (C2) a competent understanding of the concepts of complexity theory.

Mapping, of Course Learning Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1	Language theory, Regular Language: Deterministic and nondeterministic finite automata and their equivalence, conversion of deterministic and nondeterministic finite automata	CO1
2	Equivalence with regular expressions, Closure properties, The pumping lemma and applications, pushdown automata	CO2
3	Context-free Grammars and Languages: Definitions, Parse trees, Ambiguous Grammar	CO2
4	Turing Machines: basic machines, configuration, computing with Turing machines, combining Turing machines	CO3
5	The pumping lemma for CFLs and applications, Chomsky Normal forms	CO3
6	General parsing, Sketch of equivalence with pushdown automata.	CO4
7	Decidability: The undecidability of the halting problem. Reductions to other problems	CO4

Text Books, Reference Books and Other Resources:

Michael Sipser: Introduction to the Theory of Computation
Jeffrey Ullman, John Hopcroft, Rajeev Motwani: Introduction to Automata Theory, Languages, and Computation
Anil Maheshwari, Michiel Smid: Introduction to Theory of Computation

Mapping CO with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level (C: Cognitive, P: Psychomotor A: Affective)	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Practical Implementation	Class Test, Assignment Midterm, Final Exam
CO2	PO1, PO2	C4	Lecture, Practical Implementation	Class Test, Assignment Midterm, Final Exam
CO3	PO2, PO4	C4	Lecture, Practical Implementation	Class Test, Assignment Midterm, Final Exam
CO4	PO1, PO2	C2	Lecture, Practical Implementation	Class Test, Assignment Midterm, Final Exam

Course Title: Network and Computer Security

Course Code: CSE 437

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: CN

CIE Marks: 60 SEE- Marks: 40

Course Rationale:

This course is meant to offer Computer Science undergraduate students a broad overview of the field of computer security. Students will learn the basic concepts in computer security including software vulnerability analysis and defense, networking and wireless security, and applied cryptography. Students will also learn the fundamental methodology for how to design and analyze security critical systems.

Course Objectives:

The course covers principles of computer systems and network security.
We will discuss various attack techniques and how to defend against them.
Topics include network attacks and defenses, web security, e-mail, malware, mobile security and privacy.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Elaborate (C2) the concepts, issues, principles and theories of computer, web, and Internet security
CO2	Apply (C3) and explain (A3) a range of computer network security technologies and tools and services in critical situations
CO3	Analyze (C4) solutions to security challenges using common network security tools and formal methods.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Principle of Security, Security goals, Integer Algorithms, Modular Arithmetic and Linear Congruence	CO1
2.	Cryptography (Symmetric key cryptography): Data Encryption Standard (DES), Advanced Encryption Standard (AES)	CO1, CO2, CO3
3.	Cryptography (Asymmetric key cryptography): RSA, Diffie Helman	CO2, CO3
4.	Cryptosystems: ElGamal and Rabin’s Cryptosystems	CO2, CO3
5.	Authentication, Digital Signatures, Digital Signature Standard (DSS), Randomized Encryption; Rabin and ElGamal signature schemes, Cryptographically Secure Hashing; Message Authentication Codes	CO2, CO3
6.	Web/Network security: IPSec, SSL/TLS, Kerberos, PGP, Packet-Filter Firewall, Proxy Firewall, System Security Issues: Malware Analysis.	CO2, CO3
7.	Different types of web vulnerabilities, Introduction to digital forensics concepts, Security in mobile platform, mobile threats and malware.	CO2, CO3

Text Books, Reference Books and Other Resources:

Behrouz A Forouzan: Cryptography and Network Security
Contemporary Reference books/materials.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lectures notes, PPT slides, Practice Problems, Video	Assignment, Class Test, Midterm, Final Exam
CO2	PO1, PO2, PO5, PO8	C3, A3	Lectures notes, PPT slides, Practice Problems, Video	Assignment, Class Test, Midterm, Final Exam
CO3	PO2, PO4, PO5	C4	Lectures notes, PPT slides, Practice Problems, Software, Video	Assignment, Class Test, Midterm, Final Exam

Course Title: Neural Network & Fuzzy Logic

Course Code: CSE 451

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course tends to help the learners to gain knowledge about design and working principle of artificial neural network and its relation to biological neuron. It helps to develop and design different neuron models according to problem, introduces artificial intelligence and its application areas, application and evaluation of fuzzy logic and fuzzy set theories.

Course Objectives:

The objectives of this course are:

Facilitating necessary knowledge about the design and working principle of Artificial Neural Networks (ANN) and Deep Learning (DL).
Teaching the students how the mathematics behind Neural Networks (NN) Learning actually works, rather than presenting only a cursory or surface-level description.
Helping the students conceptualize and understand the key parameters in a neural network's architecture and apply them to their own applications.
Gaining the basics of different application areas and evaluation of fuzzy logic and fuzzy set theories.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Illustrate (C3) how different components are combined to form a Single-layer (Perceptron) and Multi-layer NN and their basic working mechanism.
CO2	Use (C3) the knowledge about the principle and mathematics behind the Training of a simple NN (Loss/Cost Function, Gradient Descent, Computation Graph).
CO3	Explain (C2) fuzzy systems' fundamental concepts (fuzzy number, fuzzy relation, composition of fuzzy relation).
CO4	Implement (C3) a fuzzy system representing uncertain knowledge using fuzzy rules.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	A Gentle Introduction to NN: Differences among AI/ML/DL, Subcategories of ML and their working principle, Decision Boundary, Classification Example: AND, OR, XOR, Biological Vs. Artificial NN	CO1
2.	Applications of Neural Network: An abstract example of NN, Supervised Learning with NN, Popular NN Architectures (ANN, CNN, RNN, Transformers), Reason behind Deep Learning's success	CO1
3.	Structural Building Blocks of NN: Input/Output Layers, Weights and Bias, Activation Function (non-linearity), Summation Vs. Matrix.	CO2
4.	Single Layer Perceptron (A Simple NN): Linear Regression, Training set, Input/Output, Forward Propagation, Pitfall of Single Layer Perceptron.	CO2
5.	Multi-Layer NN: Concepts of Hidden Layer, XOR Calculation Using Summation Formula, ReLU Activation Function, Vectorized Representation of Multi-Layer NN, Adding Bias as a weight.	CO2
6.	How NN's Learns: Weight Initialization (Random vs. Zero), Expected Vs Predicted Output, Loss and Cost Function, Back-propagation Basics.	CO3
7.	Logistic Regression (LR): The pitfalls of Linear Regression, Binary Classification, From Linear to Logistic Regression, Sigmoid Activation Function.	CO1
8.	Loss and Cost Function: Training/Dev/Test set, Loss function: M.S.E, Convex Vs. Non-convex, Local Vs. Global Optima, Log/Cross-Entropy Loss., L1 and L2 M.A.E, Cost Function, SoftMax Activation Function.	CO3
9.	Gradient Descent (G.D): Finding the Global Optima, Derivatives, Weights and Bias Update, Learning Rate, and Computation Graph.	CO3
10.	Backward Propagation: Back-prop using Computation Graph, G.D with Backpropagation for LR, G.D with ‘M’ training examples, G.D Algorithm using Summation, Vectorized G.D, Updating Weights and Bias, Iteration Vs. Batch Vs. Epochs.	CO3
11.	Introduction to Fuzzy Logic: Fuzzy Set, Fuzzy Set Operations - Union, Intersection, Complement, Properties of Fuzzy Set, Extension Principles, Alfa-cuts.	CO4
12.	Fuzzy Relations: Properties, Basic Operations, Compositions of Fuzzy Relations.	CO4
13.	Fuzzy Number: Representation, Properties, Addition, Subtraction of Discrete and Continuous Fuzzy Number, Addition and Subtraction of Discrete Fuzzy Number through Extension Principle, Multiplication and Division of Fuzzy Number.	CO4
14.	Fuzzy Linguistic Description: Linguistic Variables and Values, Implication Relations, Fuzzy Inference, and Composition.	CO4

Text Books, Reference Books and Other Resources:

Dive into Deep Learning – By Amazon Research Scientists.
"Make Your Own Neural Network" – By Tariq Rashid
"Fundamentals of Deep Learning", O’Reilly – By Nikhil Buduma.
"Deep learning" – By Ian Goodfellow and Yoshua Bengio and Aaron Courville.
Neural Networks and Deep Learning – By Michael Nielsen.
"Fuzzy and Neural Approach in Engineering" – Lefteri H. Tsoukalas, Robert E. Uhrig.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C3	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO2	C3	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO2	C2	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam
CO4	PO1, PO2, PO4	C3	Lecture, Problem solution, Video, Web Link	Class Test, Assignment, Midterm, Final Exam

Course Title: Neural Network & Fuzzy Logic Laboratory

Course Code: CSE 452

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite: SPL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

This subject aims to gain knowledge about design and working principle of neural network (NN) as well as practically implement NN for real-world applications.

Course Objectives:

The objectives of the course are:

To teach students the foundations of implementing NN’s using modern tools and libraries.
To disseminate the knowledge of how to appropriately report on the experiments they run, as done in academic publications.

Course Outcomes:

Upon successful completion of this course, students will be able to

CO1	· Use (C3) proper environment and software packages necessary for preprocessing, developing, and analyzing the performance of neural network.
CO2	Build (C3) a NN using modern software tools and extend it by adding multiple layers to it to understand how Multi-layer NN works.
CO3	· Investigate (C4) the results of a neural network.
CO4	· Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Environment Setup: General Issues, Setting-up Anaconda/CoLab, Recap of Python Containers and Collection Modules, NumPy Basics.	CO1, CO4
2.	Introduction to the different data sources, reading, preprocessing, and splitting the data into training and test set	CO1, CO2, CO4
3.	Building A Simple NN using latest frameworks. Compiling Multi-Layer NN and Parameter/Hyper Parameter Tuning, Regression using Neural Networks, Classification using Neural Networks, plotting loss and accuracy curve of training and validation set, Performance Measure	CO1 - CO4

Text Books, Reference Books and Other Resources:

"Deep Learning with Python" – By Francois Chollet
"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow", O’Reilly, 2nd Ed. (2019) – By Aurélien Géron
"Deep Learning from Scratch: Building with Python from First Principles" – By Seth Weidma

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO5	C3	Lecture, Lab Task	Project, Presentation, Assignment, Lab Performance, Viva
CO2	PO1, PO2, PO3, PO5, PO9	C3	Lecture, Lab Task	Project, Presentation, Assignment, Lab Performance, Viva
CO3	PO1, PO2, PO3, PO4, PO9	C4	Lecture, Lab Task	Project, Presentation, Assignment, Lab Performance, Viva
CO4	PO10	A3	Demonstration	Report

Course Title: Computer Graphics & Image Processing

Course Code: CSE 455

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SP

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Computer graphics concentrates on the fundamentals of computer graphics and addresses the knowledge and skill in computer graphics development which are essential for computing professionals. Image processing, on the other hand emphasizes on general principles of image processing and its application.

Course Objectives:

The objectives of this course are:

To develop a theoretical foundation of fundamental computer Graphics & Digital Image Processing concepts.
To learn basic algorithms for computer graphics and image processing.
To understand and imply various filters, Point processing, and Arithmetic operations in image processing.
To understand different applications of graphics and image processing.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Explain (C2) the fundamental concepts of computer graphics and image processing.
CO2	Demonstrate (C3) the scan conversion algorithms for drawing various types of geometric shapes.
CO3	Illustrate (C3) the ideas of 2D and 3D transformation and clipping techniques.
CO4	Use (C3) different types of image processing techniques for transformation, filtering, smoothing and enhancement.
CO5	Solve (C3) different image compression algorithms, segmentation and feature extraction techniques.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Fundamental Concepts: Image representation, color model, pixel, rasterization, lookup table, image types, graphical hardware, image fundamentals, sampling, quantization, mathematical tools.	CO1
2.	Scan conversion: Scan conversion algorithms for point, line, circle, ellipse, rectangle, Region filling, scan converting character, anti-aliasing	CO2
3.	2D and 3D Transformation, viewing, clipping: Geometric transformation, translation, rotation, scaling, mirror reflection, co-ordinate transformation, composite transformation, instance transformation, viewport mapping, shape clipping, clipping algorithms, curves	CO3
4.	Image processing: Fundamental concepts, color model conversion, smoothing, filtering, sharpening, fourier transformation, image enhancement, image restoration.	CO1, CO4
5.	Compression and segmentation: Huffman coding, Arithmetic coding, LZW coding, Bit-plane coding, Wavelet coding, watermarking, thresholding, segmentation, feature extraction.	CO5

Text Books, Reference Books and Other Resources:

Computer Graphics: Zhigang Xiang and Roy Plastock
Digital Image Processing: Rafael C. Gonzalez, Richard E. Woods
Computer Graphics: Principles and Practice: James D Foley, Andries Van Dan

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Slide	Class Test, Assignment, Midterm, Final
CO2	PO1, PO2	C3	Lecture, Slide	Class Test, Assignment, Midterm, Final
CO3	PO1, PO2	C3	Lecture, Slide	Class Test, Assignment, Midterm, Final
CO4	PO1, PO2	C3	Lecture, Slide	Class Test, Assignment, Midterm, Final
CO5	PO1, PO2	C3	Lecture, Slide	Class Test, Assignment, Midterm, Final

Course Title: Computer Graphics & Image Processing Laboratory

Course Code: CSE 456

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite: SPL

CIE Marks: 40 SEE Marks: 60

Course Rationale:

In Computer Graphics & Image Processing Laboratory course, the students will learn various computer graphics algorithms. In this program, the students will have to implement the mostly used computer graphics algorithms like line drawing, polygon clipping, line clipping etc. For the image processing part, students will learn about different color models and conversion among them. In this part, binary image manipulation will be performed.

Course Objectives:

The objective of the course is to enable the students to

Contribute effectively in the research area of computer graphics and image processing.
Students will learn about the practical use of various computer graphics and image processing algorithms.
Students will be able to implement new algorithms to overcome the existing problems.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Implement (C3) various scan conversion algorithms using available graphical tools.
CO2	Execute (C3) various graphical transformation methods.
CO3	Implement (C3) various image processing techniques.
CO4	Demonstrate (C3) the skills of computer graphics and image processing to develop a mini project.
CO5	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

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PO3

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✓

CO2

✓

CO3

✓

CO4

✓

CO5

✓

Course Description:

SL No.	Course Content	COs
1.	Scan Conversion Algorithm: Digital Differential Analyzer Algorithm, Bresenham Line Drawing Algorithm, Midpoint Circle Drawing Algorithm.	CO1, CO5
2.	Transformation: Geometric transformation, translation, rotation, window to viewport mapping.	CO2, CO5
3.	Image Processing: Color model, Binary image analysis, Image restoration techniques.	CO3, CO5
4.	Mini Project Development: Unity, OpenGL or any other platforms/tools.	CO4, CO5

Text Books, Reference Books and Other Resources:

Computer Graphics (Second Edition): Zhigang Xiang
Digital Image Processing: Rafael C. Gonzalez, Richard E. Woods
Computer Graphics: Principles and Practice: James D Foley, Andries Van Dan

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO5	C3	Lecture, Slide	Performance, Assignment
CO2	PO1, PO5	C3	Lecture, Slide	Performance, Assignment
CO3	PO1, PO5	C3	Lecture, Slide	Performance, Assignment
CO4	PO1, PO2, PO3, PO5	C3	Lecture, Slide	Performance, Assignment
CO5	PO10	A3	Demonstration	Report

Course Title: Control Systems

Course Code: EEE 313

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: SS

CIE Marks: 60 SEE Marks: 40

Course Rationale: This course aims to provide basic knowledge of different control techniques used in industries to control electrical and mechanical devices.

Course Objectives:

The objectives of this course are to:

Acquaint students with the basic knowledge of modern control systems.
Help the students develop their ability in developing mathematical models of electrical system.
Get idea about the principle of block diagram reduction and signal flow graphs.
Provide knowledge of the stability of electrical and mechanical systems.
Help them conceptualize the basic theories of the system analysis technique.
Make the students understand the basic knowledge of different types of controllers.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Describe (C2) the fundamentals of control systems.
CO2	Construct (C4, A4) mathematical models of different Control systems satisfying desirable control objectives.
CO3	Investigate (C4) different control systems using block diagrams and signal flow graphs.
CO4	Design (C4) different control systems using classical approaches and modern techniques.
CO5	Inspect (C4) system stability.
CO6	Explain (C1) the basics of nonlinear control.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction: Introduction to the control system, open loop and closed loop systems, the design process.	CO1
2.	Mathematical Model: Review of Laplace transform, initial and final value theorems, transfer functions: Open-loop stability, Poles Zeros state, space representation/transfer function/zero-pole of control system design; state space representation; solution of state equation.	CO2
3.	Block diagram approach: Signal flow graph; block diagram theory; block diagram reduction method;	CO3
4.	Classical Control System: Phase lead and lag controllers, Linear control system design using state feedback, Closed-loop sensitivity functions, LQR design, pole placement, lead compensation, lag compensation, lead-lag compensation.	CO4
5.	Modern Control System: Application of Eigen value and Eigen vectors, state variable analysis, canonical forms, controllability and observability, controller, and observer design, Riccati equation, data driven control basics.	CO4
6.	Stability analysis: Analysis methods such as Nyquist stability criterion, root locus, routh's criteria, Stability margins, gain and phase margin, maximum magnitude, resonant frequency.	CO5
7.	Controller Design: P, I, PI, PD, and PID types, optimum controller design, robust controller design.	CO2
8.	Non-linear control: Introduction to nonlinear control, Lyapunov stability criteria. Introduction to neural and fuzzy control.	CO6

Textbooks, References and Other Resources:

Control Systems Engineering by Norman S. Nise
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control by J. Nathan Kutz and Steven L. Brunton
Linear control system analysis and Design, John J. Azzo

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2, PO3	C4, A4	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C4	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO4	PO1, PO2, PO3	C4	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO5	PO2, PO3	C4	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam
CO6	PO1	C1	Lecture, Slide, Problem Solving, and examples	Class Test, Midterm, Assignment, Final Exam

Course Title: Control Systems Laboratory

Course Code: EEE 314

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: SSL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

This course aims to build foundation skills on designing and analyzing different industrial control systems using PLC and MATLAB.

Course Objectives:

The objectives of this course are to:

Help students demonstrate the controlling of systems using PLC
Improve the ability to present the results of investigations.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Implement (C3, P2) ladder diagram for PLC based automation system and design the best controller based on the systems requirement using CAD tool as an individual or as a member of a team.
CO2	Evaluate (C4) the results from experimental data.
CO3	Write (A3) comprehensive reports on the work done in laboratory.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Laboratory work based on theory course EEE 313. It will cover hardware-based work in PLC system.	CO1
2.	Report writing based on laboratory work.	CO2
3.	Presentation on mini project work.	CO3

Text Books, Reference Books and Other Resources:

Lab manual

CO Delivery and Assessment:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2, PO5	C3, P2	Lecture, Slide, Problem Solving, and examples	Performance, Assignment, Quiz, Viva
CO2	PO2, PO4	C4	Lecture, Slide, Problem Solving, and examples	Performance, Assignment, Quiz, Viva
CO3	PO10	A3	Demonstration	Report

Course Title: Thesis/Project

Course Code: CSE 400

Credits: 4 Class Hours/Week: 4

Course Type: Core Pre-requisite: As Set by Supervisor

Course Rationale:

This course introduces students to computer science and engineering research through planning and managing an innovative capstone research project, using research methods, and considering broader professional issues related to the project.

Course Objectives:

The objectives of the course are to:

Provide an opportunity to integrate and apply their previous engineering knowledge acquired throughout their undergraduate program to the solution of complex computing problems.
Develop creativity in analyzing and solving complex and possibly real-world or industry-related computing problems.
Develop skills to systematically design, develop, and document the solution of research projects.
Develop and enhance self-learning and life-long learning capabilities.
Make students aware of professional and ethical norms, conducts, and responsibilities in designing solutions to computing problems.
Facilitate modern software and engineering tools necessary to meet customer requirements.
Build the capacities needed to plan and manage a real-time project.
Gauge the impact of an engineering solution on the community, cultural context, physical and mental health, safety and security, environment, and sustainability.
Promote teamwork to solve real-world problems.
Develop written and verbal communication skills among students to communicate to a
variety of audiences.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Plan (C6, A3) a project considering the social, cultural, global, health, and environmental responsibilities of the professional Engineer and the principles of sustainable design and development of national and international perspective
CO2	Apply (C3, P3, A3) modern tools and techniques to manage projects under commitment to professional and ethical responsibilities
CO3	Develop (A3) the ability to communicate effectively, not only with engineers but also with the community at large as an individual and in multi-disciplinary and multi-cultural teams
CO4	Apply (C4, A4) robust project risk identification, feasibility test, assessment and treatment process
CO5	Develop (C4, A3) the capacity to undertake lifelong learning by analyzing real world situations

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.

Course Content

COs

1

The Capstone Project enables students to practice what they've learned in previous courses to the solution of a real-world computing problem. The types of research projects may include research-intensive, design, and development of a product or software or system, and solution to an engineering problem for an industry partner. The research will contribute new knowledge to the relevant field and achieve social, economic, environmental, and cultural outcomes. Students will work independently and or in a group under the guidance of a supervisor to carry out different tasks needed to solve problems and design solutions.

CO1, CO2,

CO3, CO4,

CO5

Textbooks, Reference Books and Other Resources:

World-wide web
ACM Digital Library
Digital Contents

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO2, PO3, PO6, PO7	C6, A3	Weekly meeting in-person or virtual, Web links, Reference material	Performance, Report, and Presentation
CO2	PO5, PO8	C3, P3, A3
CO3	PO9, PO10	A3
CO4	PO3, PO4, PO11	C4, A4
CO5	PO12	C4, A3	Weekly meeting in-person or virtual, Web links, Reference material	Performance, Report, and Presentation

Course Title: Compiler Construction

Course Code: CSE 453

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: ToC

CIE Marks: 60 SEE Marks: 40

Course Rationale:

Intended to enable the students the basic techniques which underlie the practice of compiler construction and use the acquired knowledge to build a compiler for a language.

Course Objectives:

The objectives of this course are to:

To introduce the major concept areas of language translation and compiler design.
Provide the foundation for understanding the theory and practice of compilers.
To learn programming language translation and compiler design concepts, language recognition,
symbol table management, semantic analysis and code generation.
To provide practical programming skills necessary for constructing a compiler.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Describe (C2) the basic concepts, principles, tools, and techniques of a compiler construction.
CO2	Apply (C3) the knowledge of different phases of a compiler to undertake language translation.
CO3	Evaluate (C5) the similarities and differences among various parsing techniques and grammar transformation techniques.
CO4	Construct (C3) context-free grammar and regular expressions to implement lexical syntactic and semantic structures.

Mapping of Course Outcomes to Program Outcomes:

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Course Description:

SL No.	Course Content	COs
1.	Introduction to compilers: Introductory concepts, Types, Applications, Phases, compiler construction tools	CO1
2.	Syntax directed translation: Construction of syntax trees, Top down translation Parser and its role, Context free grammars, Syntax analysis	CO1
3.	Lexical analysis: Role of the lexical analyzer, Token specification, Recognition of tokens, Symbol table, Finite automata	CO2, CO4
4.	Context free grammar: Syntax error handling, Ambiguity, left recursion, Left factoring, Different types of parser.	CO3, CO4
5.	Intermediate code generator: DAG, three address code, code generation, Addressing mode, instruction cost, basic block, Flow graph.	CO2, CO3
6.	Code optimization: Basic concepts, Code optimization techniques	CO2
7.	Syntax directed translation: Syntax directed definition, Semantic rules analysis, Dependency graph, Annotated parse tree, implementation of SDT	CO1

Text Books, Reference Books and Other Resources:

Aho, Ulman & Ravishethi: Principal of Compiler Design.
Philip: Compiler Design Theory.
Willam A. Barrette: Compiler Construction, Theory and Design.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture notes, Slide	Class Test, Midterm, Assignment, Final Exam
CO2	PO1	C3	Lecture notes, Slide	Class Test, Midterm, Assignment, Final Exam
CO3	PO2, PO4	C5	Lectures notes, Slide	Class Test, Midterm, Assignment, Final Exam
CO4	PO1, PO2	C3	Lectures notes, Slide	Class Test, Midterm, Assignment, Final Exam

Course Title: Compiler Construction Laboratory

Course Code: CSE 454

Credits: 1.5 Class Hours/Week: 3

Course Type: Core Pre-requisite: ToC

CIE Marks: 40 SEE Marks: 60

Course Rationale:

Intended to enable the students the basic techniques which underlie the practice of compiler construction and use the acquired knowledge to build a compiler for a language.

Course Objectives:

The objectives of this course are:

To introduce the major concept areas of language translation and compiler design.
Provide the foundation for understanding the theory and practice of compilers.
To learn programming language translation and compiler design concepts, language recognition, symbol table management, semantic analysis and code generation.
To provide practical programming skills necessary for constructing a compiler.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Apply (C3) software tools and techniques to construct compiler
CO2	Apply (C3) syntax analyzer, parser, semantic analyzer and various parsing techniques in Compiler Design
CO3	Apply (C3) mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems.
CO4	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

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CO3

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CO4

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Course Description:

SL

No.

Course Content

COs

1.

Laboratory work based on theory course Compiler Construction

CO1, CO2, CO3

2.

Report generation based on Lab work

CO4

Text Books, Reference Books and Other Resources:

Aho, Ulman & Ravishethi: Principal of Compiler Design.
Philip: Compiler Design Theory.
Willam A. Barrette: Compiler Construction, Theory and Design

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO5	C3	Problem analysis, Demonstration, Problem solving	Performance, Report, Quiz
CO2	PO1, PO2, PO5	C3	Problem analysis, Demonstration, Problem solving	Performance, Quiz, Report
CO3	PO1, PO2, PO5	C3	Problem analysis, Demonstration, Problem solving	Performance, Quiz, Report
CO4	PO10	A3	Demonstration	Report

Course Title: Machine Learning

Course Code: CSE 457

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: AI

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course provides appropriate machine learning techniques, and learning algorithm to best suit the current need and enhance the learning parameters for maximum performance.

Course Objectives:

Providing the students with an in-depth introduction to two main areas of Machine Learning: supervised and unsupervised
Introducing some of the main models and algorithms for regression, classification, and clustering
Developing an understanding of the theoretical relationships between the learning algorithms
Enhancing the learning parameters to achieve maximum performance

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) the key concepts such as supervised learning, unsupervised learning, reinforcement learning, feature engineering, model evaluation, and trade-off bias and variance.
CO2	Apply (C3) supervised learning, unsupervised learning and reinforcement learning algorithms to datasets.
CO3	Analyze (C4) data for preprocessing and cleaning datasets to prepare them for machine learning tasks, gain insights and identify patterns in the data.
CO4	Evaluate (C5) and optimize the performance of machine learning models using model selection, hyperparameter tuning, model validation, and metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC).

Mapping of Course Outcomes to Program Outcomes:

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PO3

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PO6

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Course Description:

SL No.	Course Content	COs
1.	Introduction to Machine Learning: Classification of learning, Unsupervised and supervised learning	CO1, CO2
2.	Linear Regression with One Variable: application of linear regression, cost function, and gradient descent method for learning	CO2, CO3, CO4
3.	Linear Regression with Multiple Variables, Logistic Regression, and Regularization;	CO2, CO3, CO4
4.	Neural Networks: Representation, Learning, Multilayer feed forward network, Back propagation algorithm for training a feed forward network	CO2, CO3, CO4
5.	Applications: Advice for Applying Machine Learning, Machine Learning System Design, Support Vector Machines	CO3, CO4

Text Books, Reference Books and Other Resources:

Pattern Recognition and Machine Learning by Christopher M. Bishop
Machine Learning: A Probabilistic Perspective, by Kevin Murphy.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture, Problem solution, Video, Web Link	Class test, Midterm, Assignment, Final
CO2	PO1, PO2	C3	Lecture, Problem solution, Video, Web Link	Class test, Midterm, Assignment, Final
CO3	PO2, PO4	C4	Lecture, Problem solution, Video, Web Link	Class test, Midterm, Assignment, Final
CO4	PO2, PO4	C5	Lecture, Problem solution, Video, Web Link	Class test, Midterm, Assignment, Final

Course Title: Machine Learning Laboratory

Course Code: CSE 458

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite: AIL

CIE Marks: 40 SEE Marks: 60

Course Rationale:

This subject provides most effective machine learning techniques, learning algorithm to best suit the current need and enhance the learning parameters for maximum performance.

Course Objectives:

The objectives of the course are:

To provide students with an in-depth introduction to two main areas of Machine Learning: supervised and unsupervised.
To apply some of the main models and algorithms for regression, classification, and clustering.
To enhance the learning parameters to achieve maximum performance.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	· Implement (C3) different machine learning algorithms for classification, regression, clustering, and dimensionality reduction using modern tools and libraries such as scikit-learn, TensorFlow and/or PyTorch etc.
CO2	Apply (C3) appropriate methods for handling missing data and dealing with class imbalance using the above stated tools
CO3	Apply (C3) appropriate mitigation techniques to solve the issues of overfitting, underfitting, bias, and variance in machine learning models using the same tools
CO4	Evaluate (C5) the performance of different machine learning algorithms using cross-validation and other validation techniques using the mentioned tools
CO5	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

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PO12

CO1

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CO2

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CO5

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Course Description:

SL No.	Course Content	COs
1.	Data Preprocessing, Regression, SVM, KNN, Decision Tree, Naïve Bayes classification, K-Means Clustering	CO1 – CO4
2.	Association Rule Learning: Apriori, Eclat, PCA, Model Selection and Boosting	CO1 – CO4
3.	Report preparation based on lab work	CO5

Text and Reference books:

Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller, Sarah Guido
Machine Learning in Action - Peter Harrington
Python Machine Learning - By Sebastian Raschka

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2, PO5	C3	Demonstration, Discussion, Experiment	Performance, Assignment, Report
CO2	PO1, PO2, PO5	C3	Demonstration, Discussion, Experiment	Performance, Assignment, Report
CO3	PO1, PO2, PO5	C3	Demonstration, Discussion, Experiment	Performance, Assignment, Report
CO4	PO1, PO2, PO4, PO5	C5	Demonstration, Discussion, Experiment	Performance, Assignment, Report
CO5	PO10	A3	Demonstration	Report

Course Title: Pattern Recognition

Course Code: CSE 459

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: AI

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course will study state-of-the-art techniques for analyzing data. The goal is to extract meaningful information from feature data. This includes statistical and information theoretic concepts relating to machine learning, data mining and pattern recognition.

Course Objectives:

The main objectives of this course are:

The students should have a clear understanding of the design and construction and a pattern recognition system.
To introduce the major approaches of pattern recognition.
Finally, the student will have a clear working knowledge of implementing pattern recognition techniques.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to

CO1	Demonstrate (C2) the essential principles, theories, and techniques underlying pattern recognition: statistical modeling, machine learning algorithms, and data analysis methods.
CO2	Implement (C3) pattern recognition systems considering data preprocessing, extract relevant features, and train models using various algorithms.
CO3	Apply (C3) pattern recognition algorithms and tools to real-world problems: computer vision, speech recognition, natural language processing, and bioinformatics.
CO4	Assess (C5) the performance of pattern recognition algorithms using appropriate evaluation metrics.
CO5`	Comprehend (C2) the ethical and social implications of pattern recognition such as privacy, bias, and fairness.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

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√

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Course Description:

SL No.	Course Content	COs
1.	Introduction: Definitions, Datasets for Pattern Recognition, Different Paradigms of Pattern Recognition and Machine Learning, Data Normalization, Hypothesis Evaluation, VC-Dimensions and Distribution, Bias - Variance Tradeoff, Regression (Linear)	CO1
2.	Feature Selection and Dimensionality Reduction: PCA, LDA, ICA, SFFS, SBFS Additional Features and Template Matching: Texture, Shape and Size Characterization, Fractals, Features for Audio	CO2
3.	Discriminative Methods: Distance-based methods, Linear Discriminant Functions, Decision Tree, Random Decision Forest and Boosting. Bayes Decision Theory: Bayes decision rule, Minimum error rate classification, Normal density and discriminant functions, Bayesian networks Parameter Estimation: Maximum Likelihood and Bayesian Parameter Estimation Artificial Neural Networks: MLP and Backpropagation Kernel Machines: Kernel Tricks, Support Vector Machines Clustering: k-means clustering, Gaussian Mixture Modeling	CO3
4.	Confusion Matrix in Machine learning, Receiver Operating Characteristic (ROC) Curve	CO4
5.	Legal and Ethical consideration in pattern recognition	CO5

Text and Reference Books:

Duda, R.O., Hart, P.E., and Stork, D.G. (2001), Pattern Classification. Wiley-Interscience. (2nd Edition)
Devi V.S.; Murty, M.N. (2011), Pattern Recognition: An Introduction, Universities Press, Hyderabad.
I. Kuncheva (2004.), Combining Pattern Classifiers: Methods and Algorithms, Wiley-Blackwell

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO1, PO2	C3	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO4	PO2, PO4	C5	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam
CO5	PO4, PO6, PO8	C2	Lecture notes, PPT slides, problem solving, web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Pattern Recognition Laboratory

Course Code: CSE 460

Credits: 01 Class Hours/Week: 2

Course Type: Core Pre-requisite: AIL

CIE Marks: 30 SEE Marks: 70

Course Rationale:

This subject aims to practically implement as well analyze the various procedures, methods and algorithms related to pattern recognition for specific application.

Course Objectives:

The objective of the course is to enable the students to

Have a clear understanding of the design and construction and a pattern recognition system.
Introduce the students, the major approaches in template and statistical pattern recognition.
Have a clear working knowledge of implementing pattern recognition techniques and the scientific python computing environment.

Course Outcomes:

Upon successful completion of this course, students will be able to

CO1	Apply (C3) pattern recognition algorithms to real-world datasets: preprocess data, select appropriate features, train models, and evaluate the performance of the algorithms.
CO2	Use (C3) pattern recognition tools and software packages to implement and test different algorithms, analyze the results, and visualize the patterns discovered.
CO3	Analyze (C4) complex datasets to extract meaningful insights, identify relevant patterns, and make informed decisions based on the analysis.
CO4	Report (A3) project work both written and verbally.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

Course Description:

SL No.	Course Content	COs
1	Image Manipulation, Enhancement, Filtering, Convolution, Segmentation, Feature Extraction, Feature Selection	CO1, CO4
2	Supervised classification, Unsupervised clustering, Artificial Neural Network, Convolutional Neural Networks: elements, architectures, convolution visualization; Software Packages and tools: Tensorflow, Keras, Sklearn, NumPy, Matpoltlib	CO2, CO4
3	Design a project on open-source dataset using state-of-the-art models	CO3, CO4

Text and Reference Books:

Duda, R.O., Hart, P.E., and Stork, D.G. (2001), Pattern Classification. Wiley Interscience. 2nd Edition.
Devi V.S.; Murty, M.N. (2011), Pattern Recognition: An Introduction, Universities Press, Hyderabad.
I. Kuncheva (2004.), Combining Pattern Classifiers: Methods and Algorithms, Wiley-Blackwell

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s taxonomy domain/level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2, PO5	C3	Demonstration, Discussion, Experiment	Report, Performance, Presentation, Viva
CO2	PO1, PO2, PO5	C3	Demonstration, Discussion, Experiment	Report, Performance, Presentation, Viva
CO3	PO4, PO5	C4	Demonstration, Discussion, Experiment	Report, Performance, Presentation, Viva
CO4	PO9, PO10	A3	Demonstration	Report

Course Title: Contemporary Courses of Computer Science

Course Code: CSE 481

Credits: 3 Class Hours/Week: 3

Course Type: Core Pre-requisite: CN

CIE Marks: 60 SEE Marks: 40

Course Rationale:

This course is intended for the students who seek an overall understanding of cloud computing core concepts, architecture, its history, popular cloud services, security, pricing, and the technologies enabled by cloud computing.

Course Objectives:

Identify and demonstrate different cloud computing and cloud deployment models.
Comprehend the compute, storage, database, networking, and content delivery facilities offered by the underlying cloud infrastructure.
Identify security and privacy issues in cloud computing.
Analyze the automatic scaling and monitoring performance of the underlying cloud technologies
Implement applications for different cloud platforms.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Define (C1) the key concepts, standards, and technologies in cloud computing.
CO2	Describe (C2) the performance, scalability, and availability of the underlying cloud technologies.
CO3	Compare (C4) resource usage in cloud computing infrastructures for different scientific applications.
CO4	Describe (C2) privacy and security issues for cloud infrastructure and virtual environments.
CO5	Design (C6) solutions for complex cloud hosting problems.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

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PO12

CO1

√

CO2

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CO3

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CO4

√

CO5

√

Course Description:

SL No.	Course Content	COs
1.	Introduction to Cloud Computing: Grid Computing, Distributed computing, Cloud computing models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS)	CO1
2.	Cloud deployment models: public, private, and hybrid models. Example cloud platforms (e.g. AWS, Google Cloud Platform, Microsoft Azure), and Interoperability. Cloud Economics and Billing: Pricing Fundamentals, Cost of ownership, On-premises vs. Cloud solution, Organizations and Accounts, Billing and Cost management	CO1
3.	Cloud infrastructure: Points of presence, Region, Availability Zones, Data Centers, Edge Locations	CO1
4.	Cloud Security: Shared responsibility in the cloud, Identity and Access Management, Securing Users, Securing Data	CO4
5.	Networking and Content Delivery: Virtual Private Cloud (VPC), VPC networking, VPC sharing, VPC Peering, Site-to-site VPN, VPC Transit Gateway, Connecting on-premises data center to cloud, VPC Security, Routing Tables, Security groups, ACLs, Routing and DNS, Content Delivery Network, Pricing Transit Gateway	CO2, CO4
6.	Compute: EC2, EC2 Cost Optimization, Types of EC2 instances, Container Services, Docker, Serverless Computing: AWS Lamda and Elastic Beanstalk, key management services	CO3, CO5
7.	Cloud Storage: Object Storage, Block Storage, Network File System, Long Term Storage, automatic scaling of storages, Lifecycle management, Types of Object Storage, Use cases of different types of storages	CO3
8.	Cloud Database: Relational/SQL Database, NoSQL databases, Data storage for OLTP systems, warehouse, automatic scaling database systems, Reliability, Availability, Replication.	CO2
9.	Cloud Architecture: Well-Architected Framework, Pillars of the framework, Reliability and Scalability, Cloud Resource Provisioning	CO2, CO3
10.	Load Balancing, Auto Scaling, Dynamic Scaling, Cloud Performance Monitoring	CO2

Text Books, Reference Books and Other Resources:

AWS Cloud Fundamentals Online Course Material
Cloud Computing: A Hands-On Approach, by Arshdeep Bahga and Vijay Madisetti.
Cloud Computing Theory and Practice, by Dan Marinescu, 2nd Edition.
Cloud Computing: Principles and Paradigms, by Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, 1st Edition, Wiley

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C1	Lecture notes, PPT slides, problem solving, AWS web content	Class Test, Midterm, Assignment, Final Exam
CO2	PO1, PO2	C2	Lecture notes, PPT slides, problem solving, AWS web content	Class Test, Midterm, Assignment, Final Exam
CO3	PO2, PO4	C4	Lecture notes, PPT slides, problem solving, AWS web content	Class Test, Midterm, Assignment, Final Exam
CO4	PO1, PO2	C2	Lecture notes, PPT slides, problem solving, AWS web content	Class Test, Midterm, Assignment, Final Exam
CO5	PO3, PO4, PO5	C6	Lecture notes, PPT slides, problem solving, AWS web content	Class Test, Midterm, Assignment, Final Exam

Course Title: Contemporary Courses of Computer Science Laboratory

Course Code: CSE 482

Credits: 1 Class Hours/Week: 2

Course Type: Core Pre-requisite: CNL

CIE Marks: 70 SEE Marks: 30

Course Rationale:

This course enables the students to become familiar with web application implementation and hosting, and service configuration in real-world cloud platforms.

Course Objectives:

Become familiar with virtual machine configuration in different cloud platforms.
Demonstrate the advantage of managed cloud services.
Implement custom web applications in different cloud computing service models.
Use advanced techniques to scale cloud resources under dynamic requirements.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Configure (C3) virtual machines (instances) with underlying storage mechanisms, supporting security and access mechanisms.
CO2	Leverage (C3) managed database services for solving relational database needs.
CO3	Implement (C3) web applications in IaaS and PaaS environments.
CO4	Use (C3) load balancing and auto-scaling to automatically control virtual instances and distribute traffic across the instances.
CO5	Design (C6) a custom cloud hosting solution for a given problem.
CO6	Report (A3) lab activities and experimental results or findings.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

✓

CO2

✓

CO3

✓

CO4

✓

CO5

✓

CO6

✓

Course Description:

SL No	Course Content	COs
1.	Introduction to AWS IAM	CO1, CO5, CO6
2.	Network and Content Delivery: Build VPC and Launch a Web Server	CO1, CO5, CO6
3.	Introduction to Amazon EC2, AWS Lambda, AWS Elastic Beanstalk	CO2, CO3, CO5
4.	Working with EBS	CO1, CO5, CO6
5.	Build a DB Server and Interact with the DB Using an App	CO2, CO5, CO6
6.	Scale and Load Balance Cloud Architecture	CO4, CO5, CO6

Text Books, Reference Books and Other Resources:

AWS Cloud Fundamentals Online Course Material
Cloud Computing: A Hands-On Approach, by Arshdeep Bahga and Vijay Madisetti.
Cloud Computing Theory and Practice, by Dan Marinescu, 2nd Edition.
Cloud Computing: Principles and Paradigms, by Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, 1st Edition, Wiley

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO5	C3	Demonstration, discussion, experiment	Performance, Lab, Quiz
CO2	PO1, PO5	C3	Demonstration, discussion, experiment	Performance, Lab, Quiz
CO3	PO1, PO5	C3	Demonstration, discussion, experiment	Performance, Lab, Quiz
CO4	PO1, PO5	C3	Demonstration, discussion, experiment	Performance, Lab, Quiz
CO5	PO1, PO2, PO3, PO4, PO5	C6	Demonstration, discussion, experiment	Performance, Lab, Quiz
CO6	PO10	A3	Demonstration	Report

Non-Engineering

Course Title: Basic Accounting

Course Code: ACC 101

Credits: 3	Class Hours/Week: 3
Course Type: Non-engineering	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course helps students to get basic knowledge of accounts and other business studies.

Course Objectives:

The objectives of this course are:

To enable the students to study, understand and use the accounting process and accounting information.
To introduce the students to accounting as a valuable tool of business, record keeping, generation of accounting information, analysis and interpretation and communication of the same to different interest groups and thereby achieving the predetermined goals of the organization.
To teach the causes of non-equal balance of bank account of a company account and bank statement and also understand the bank transaction and function of bank.
To measure cost per unit and total cost of a product.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) various concepts related to accounting system.
CO2	Calculate (C3) the recording process of accounting system.
CO3	Demonstrate (C3) the process of preparing financial statement for a problem.
CO4	Illustrate (C3) various concepts and process of depreciation.
CO5	Solve (C3) various problems of cost accounting and standard coding.

Mapping of Course Outcomes to Program Outcomes:

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PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

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CO1

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CO2

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CO3

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CO4

√

CO5

√

Course Description:

SL No.	Course Content	COs
1.	Introduction: Definition of Accounting and Financial Accounting, Users of Accounting Information, Branches of Accounting, Types of accounts, Determination of debit and credit, Accounting concepts and Conventions, Definition of business transaction, Nature of business transaction, Accounting Principles, GAAP, Accounting Cycle.	CO1
2.	Recording Process: Journal, Ledger, Trial Balance, Cash Book, adjusting entries, closing entries, classifying capital and revenue expenditure	CO2
3.	Preparation of Financial Statement: Definition, Features of financial statement, Component/parts of financial statement, Multi-step Income Statement, Owners Equity Statement, Classified Balance Sheet, Cash Flow Statement, Bank Reconciliation Statement.	CO3
4.	Depreciation: Definition of Depreciation, Objectives and methods for providing depreciation.	CO4
5.	Cost Accounting: Introduction, Objectives and advantages of cost accounting, Elements of cost, Preparation of cost sheet, Inventory valuation (Store ledger), Overhead allocation.	CO5
6.	Standard Costing: Calculation of material, labor and overhead variance, Break-even point, Margin of safety, Use of accounting information in project evaluation, Budget (cash budget and master budget).	CO5

Text Books, Reference Books and Other Resources:

Accounting Principles. by Weygandt, Kieso and Kimmel- Latest edition
Cost Accounting by Bhabatosh Banarjee

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1, PO2	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO2	PO1, PO2	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO3	PO1, PO2	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO4	PO1, PO2	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO5	PO1, PO2	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam

Course Title: Basic Economics

Course Code: ECO 201

Credits: 3	Class Hours/Week: 3
Course Type: Non-engineering	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is a foundation course that will prepare students to understand fundamental microeconomic and macroeconomic concepts. The goal of this course is to provide some indispensable lessons about individual decision making, the role of the market as well as government in an economy.

Course Objectives:

The main objectives of this course are:

Edify the concept of fundamental demand and supply mechanism in relation to individual decision making.
Figure out the measurement of individual satisfaction and find out basic economic problems of society as well as make clear the basic ideas on firm and production.
Understand and verify the objectives, goals, tools, and problems of macroeconomic concepts and public policies.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Explain (C2) Demand and supply concept as an apparatus of individual decision making and express individual satisfaction level numerically, graphically.
CO2	Describe (C2) the acquired concepts to understand the nature of firm and production.
CO3	Summarize (C2) the advantages and disadvantages of different macroeconomic situations and identify required policies.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

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PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.

Course Content

COs

1.

The Economic Problem, The Language of economics, Economic System, How the Economy works, Demand and Supply Theory, Elasticity, Consumer behavior and market demand, Production and Costs Theory, The perfectly competitive market and its characteristics, Imperfectly competitive markets, Basic principles of factor pricing, Resource allocation and the market, International trade, Non – Renewable resources, Key macroeconomics phenomena, Income determination, Balance of payments and the exchange rates

CO1,

CO2, CO3

Text Books, Reference Books and Other Resources:

Gregory Mankiw: Principles of Economics; 6^th edition
J. Baumol and A.S. Blinder: Economics: Principles and Policies
Robin Bade and Michael Parkin: Foundations of Microeconomics, 8^th Edition
Fisher, R. Dornbusch and Schmalensee: Economics, McGraw Hill Company, New York
Modern Microeconomics: Theory and Application. H.L. Ahuja and S. Chand. Revised Edition.

CO Delivery and Assessment:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO2	PO1	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO3	PO1	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam

Course Title: Industrial & Business Management

Course Code: MGT 203

Credits: 3	Class Hours/Week: 3
Course Type: Non-engineering	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course is designed to familiarize students with the current management practices and the business environment in the modern world.

Course Objectives:

The main objectives of this course are:

Introduce students to the industrial management concepts and functional areas of business.
Acquaint students with the management skills needed for different organizational activities.
Provide the knowledge of organizational structure & design
Impart knowledge on basic Marketing & HRM concepts and functions
Provide an understanding of financial and operational management tools and techniques.
Apply the knowledge of organizational innovations to gain competitive advantages
Facilitate students with necessary knowledge regarding leadership and ethical issues in organizations.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Demonstrate (C3) an understanding of industrial management concepts and practices.
CO2	Identify (C1) the basic managerial role, skills, functions & the nature of managerial work for upcoming challenges in an organization.
CO3	Analyze (C4) the implication of numerous elements of organizational structure & design.
CO4	Explain (C2) the basic principles and functions of marketing & HRM.
CO5	Understand (C2) and apply (C3) a variety of technical and analytical tools used by the financial and operations managers for decision making.
CO6	Understand (C2) how to integrate organizational innovations to gain competitive advantages.
CO7	Apply (C3) appropriate leadership practices and ethics in complex business environment.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

CO4

√

CO5

√

CO6

√

CO7

√

Course Description:

SL No.	Course Content	COs
1.	Introduction: Basic concepts-Industry, Business, management, industrial management, Types of Industry, why study industrial business management, objectives, Importance, Applications, Problems of industrial management.	CO1
2.	Management: Nature of management, levels of management, managerial roles, managerial skills, organizational hierarchy, managerial functions, the science and art of management, productivity, effectiveness and efficiency. Management theories-Classical & behavioral management, Principles of management, challenges of a manager.	CO1
3.	Organization: Meaning, organizational structure, departmentalization, work specialization, chain of command, span of control, centralization vs decentralization, formalization, common organizational design, simple structure, bureaucracy, matrix structure, team structure, virtual organization, boundary less organization. learning organization.	CO2
4.	Marketing: Definition, basic concepts (needs, wants and demands), marketing strategy: segmentation; targeting; positioning, marketing mix, holistic marketing, green marketing.	CO4
5.	Financial Management: Functions & scope, key activities of financial managers, principles of financial management, financial statements and reports, financial ratio analysis, types of financial ratios: liquidity ratios; asset management ratios; profitability ratios and market ratios, net present value (NPV), internal rate of return (IRR), break-even analysis, depreciation, time value of money, future value vs present value.	CO5
6.	Human Resource Management: Meaning, HRM planning, job analysis, recruitment, selection, selection process, selection techniques, employee training and development, performance appraisal, conflict management, health and safety measures.	CO4
7.	Operations Management: Definition, facility location, plant layout, inventory management, productivity, quality theories and characteristics, project planning (PERT, CPM).	CO5
8.	Innovation Management: Innovation and discovery, sources of innovative, types of innovation idea, role of creative thinking, creative climate, innovation process, principles of innovation, product life cycle, service innovation, intellectual property, patent, copyrights.	CO6
9.	Leadership & Ethics in Business: Definition and components, trait versus process leadership, assigned versus emergent leadership, leadership and power, leadership and management, transformational leadership, servant leadership, team leadership. Concept, domains of ethical theories, principles of personal & professional ethics, concepts of business ethics, importance of business ethics, values & ethics, characteristics of an ethical organization, legal vs ethical.	CO7

Text Books, Reference Books and Other Resources:

Griffin, R. W. (2005). Management (8th ed.). Houghton Mifflin Company.
Robins, S. P., & Coulter, M. (2004). Management (8th ed.). Prentice Hall.
Lee, J. K., Larry, P. R., & Manoj, K. M. (2006). Operations Managemen: Processes & Value Chains (8th ed.). New Jersey, USA: Prentice Hall.
Kotler, P., Armstrong, G., Agnihotri, P. Y., & Haque, E. (2010). Principles of Marketing: A South Asian Perspective (13th ed.). Delhi: Pearson Prentice Hall.
Brigham, E. F., & Houston, J. F. (2009). Fundamentals of Financial Management (10th/Latest ed.). Cengage Learning.

CO Delivery and Assessment:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level *(C: Cognitive, P: Psychomotor A: Affective)*	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO2	PO1	C1	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO3	PO1	C4	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO4	PO1	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO5	PO1	C2, C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO6	PO1	C2	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam
CO7	PO1	C3	Lecture, PPT slides, problem solving	Class Test, Assignment, Midterm, Final Exam

Course Title: Organizational Behavior

Course Code: MGT 251

Credits: 3	Class Hours/Week: 3
Course Type: Non-engineering	Pre-requisite:
CIE Marks: 60	SEE Marks: 40

Course Rationale:

This course helps students to identify the human behavior in organizational settings. It studies individual and group behavior, organizational communication and structure, conflicts, and stress have on behavior within organization and the proper use of such knowledge improve an organization’s effectiveness.

Course Objectives:

The main objectives of this course are:

Identify the complex nature of human beings in organizations through causes and effects of that behavior.
Provide the knowledge of organizational communication, structure and culture.
Discuss conflict resolution and change management in order to indoctrinate students how to handle a new workforce and cope with the challenges of new environment.
Acquaint students with numerous approaches of stress management.

Course Outcomes (COs):

Upon successful completion of this course, students will be able to:

CO1	Describe (C2) numerous concepts, factors, challenges related to human behavior in organization.
CO2	Explain (C2) the individual and team behavior in the organization.
CO3	Discuss (C2) the structure and process of organization.

Mapping of Course Outcomes to Program Outcomes:

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

√

CO2

√

CO3

√

Course Description:

SL No.	Course Content	COs
1.	Introduction: Meaning of OB, importance, goals of OB, developing an OB model, challenges and opportunities, organizational culture and its impacts, socialization practice, globalization, cultural dimension, cross-cultural management	CO1
2.	Behavior within Organization (The Individual): Workplace diversity, workplace discrimination, biographical characteristics, characteristics factor, intellectual and physical abilities, attitudes, job satisfaction, emotions and mood, affective events theory, emotional intelligence, personality, Myers-Briggs Type Indicator (MBTI) personality framework and the Big Five mode, core self-evaluation (CSE), self-monitoring, proactive personality, Hofstede’s five value dimensions and the GLOBE framework, perception, attribution theory, decision making, three-stage model of creativity, Contrast Effect, Halo Effect, Stereotyping. motivation, different motivational theories, justice, job engagement, changing work environment	CO2
3.	Behavior within Organization (Group and Interpersonal Influence): Types of groups, group development model, groups vs team, group effectiveness, group decision making, team arrangement, communication, types of communication, interpersonal communication, barriers to effective communication, cross-cultural communication, leadership, different theories of leadership, roles of leaders, leadership vs power, types of power, abuse of power, politics and political behavior, conflict, types and loci of conflict, conflict process, bargaining, negotiation, organizational structure, elements and characteristics of organizational structure, structural models, downsizing and its effects, behavioral implications	CO2
4.	Organizational Structure and Process: Organizational culture and its effects, factors for organizational culture, types of culture, effect of national culture on organizational culture, recruitment and selection methods, types of training, methods for performance evaluation, leadership role of HR, Forces for change, individual and organizational resistance to change, overcoming resistance to change. Stress management: Definition, Sources and Consequences, Managing Stress: Individual Approach and Organizational Approach.	CO3

Text Books, Reference Books and Other Resources:

Organizational Behavior. 15^thed. New Delhi (2014): Pearson/Prentice Hall by Robbins, S. P., Judge, T.A & Vohra, N.
Organizations: behavior, structure, processes. 12^thed. McGraw Hill (2006) by Gibson, J., Ivanchevich, J., Donnelly, J. &Konopaske R.

Mapping Course Outcomes with the Teaching-Learning and Assessment Strategy:

COs	Corresponding POs	Bloom’s Taxonomy Domain/Level (C: Cognitive, P: Psychomotor A: Affective)	Delivery Methods and Activities	Assessment Tools
CO1	PO1	C2	Lecture, Notes, Case Studies	Class Test, Assignment, Mid Term, Final Exam
CO2	PO1	C2	Lecture, Notes, Case Studies	Class Test, Assignment, Mid Term, Final Exam
CO3	PO1	C2	Lecture, Notes, Case Studies	Class Test, Assignment, Mid Term, Final Exam

Teaching Learning & Evaluation:

Grading System

Grading Scale: 4.00 Grades: The performance of a student in a given course is made through continuous and summative assessments. That comprises quizzes / in-course, class participation, attendance, home work/assignment, case study, class test, mid-term and semester final examinations. Letter grades and grade points are used to evaluate the performance of a student in a given course. A+, A, A-, B+, B, B-, C+, C and D are the passing grades while F is the failing grade. Letter grades and corresponding numerical grades used in calculating the GPA/CGPA (Cumulative grade point average) are as follows:

Marks Range	Letter Grade		Grade Point
80% and above	A+	A Plus	4.00
75% to less than 80%	A	A Regular	3.75
70% to less than 75%	A-	A Minus	3.50
65% to less than 70%	B+	B Plus	3.25
60% to less than 65%	B	B Regular	3.00
55% to less than 60%	B-	B Minus	2.75
50% to less than 55%	C+	C Plus	2.50
45% to less than 50%	C	C Regular	2.25
40% to less than 45%	D	D Regular	2.00
Less than 40%	F		0.00

Grade Point Average (GPA) and Cumulative Grade Point Average (CGPA):

Grade Point Average (GPA) is the weighted average of the grade points obtained in all the courses attempted by a student. The four-step procedure that will be followed to calculate the CGPA (Cumulative Grade Point Average) of a student is given below:

Grade points earned in each course will be computed by multiplying the credit (C_i) and the individual grade point (G_i) earned in that course (i.e., C_i*G_i).
The grade points (determined in step i) of all the attempted courses will be added to determine the total grade point earned (i.e., ∑C_i*G_i).

Credits of all courses will be added together to determine the total credits (∑C_i).

CGPA will be determined by dividing the result of step (ii) by the result of step (iii). For example, if a student attempted n courses in a semester having credits C₁, C₂, … , C_n and his/her grade points in these courses are G₁, G₂, … , G_n respectively, then the CGPA can be calculated as follows:

CGPA =

A Numerical Example

Suppose a student has completed six courses in a semester and obtained the following grades:

Course	Credit *C_i*	Letter Grade	Grade Point *G_i*	**C_i G_i***
MAT 111	3.0	A+	4.00	12.00
ECO 101	3.0	A	3.75	11.25
CSE 101	3.0	A+	4.00	12.00
STA 101	3.0	F	0.00	0.00
ENG 101	3.0	A	3.75	11.25
MAT 121	1.5	B	3.00	4.50
Total	∑*C_i*= 16.5	∑ **C_i G_i*** = 51.00

CGPA = (51.00 / 16.50) = 3.09

Note: If the 3rd digit after decimal points is above ‘0’, grade will be rounded (ceiling) into the second digit after decimal. For example, 2.990 will be counted as 2.99 while 2.991 will be counted as 3.00 in CGPA calculation.

Teaching-Learning and Student Assessment Strategy for Theory Courses

Delivery Methods and Activities

Lecture
Digital Learning
Video or Animation
Interactive Lecture
Mathematical Problem solving
Group Discussion
Student Presentation

Student Assessment Tools:

Class Assessment - 10%
Assignment/ Class Performance - 10%
Class test- 20%
Mid Term-20%
Final Exam -40%

Teaching-Learning and Student Assessment Strategy for Laboratory Courses (Hardware)

Delivery Methods and Activities

Interactive Lecture
Experiment done by Students
Student Presentation
Student Demonstration

Student Assessment Tools:

Class Assessment - 10%
Lab Performance - 40%
Report- 20%
Quiz/Written Exam-20%
Viva -10%

Teaching-Learning and Student Assessment Strategy for Project Based Laboratory Courses (Hardware/Programming)

Delivery Methods and Activities

Interactive Lecture
Experiment done by Students
Student Presentation
Student Demonstration
Project

Student Assessment Tools:

Class Assessment - 10%
Lab Performance - 40%
Project- 30%
Presentation/Viva-20%
Report -20%

Teaching-Learning and Student Assessment Strategy for Laboratory Courses (Programming):

Delivery Methods and Activities

Interactive Lecture
Student Demonstration
Discussion
Experiment done by Students

Student Assessment Tools:

Class Assessment - 10%
Lab Performance - 20%
Report- 10%
Lab Exam-30%
Quiz/Written Exam-20%
Viva -10%

Teaching-Learning and Student Assessment Strategy for Seminar Type Courses:

Delivery Methods and Activities:

Lecture
Video Presentation
Interactive Discussion
Pair work
Audiotapes
Presentation
Selected Stories

Student Assessment Tools:

Class Assessment -10%
Class participation/ Group work -30%
Quiz/Class Test/Assignment- 30%
Presentation- 20%
Viva - 10%

Teaching-Learning and Student Assessment Strategies for Project/Thesis:

Teaching-Learning Strategy

Instructions
Reading Materials/PPT slides
Tutorials, E-Learning
Student Presentation
Group Work: Assignments, Discussions, Investigations, Presentations
Case Studies

Assessment Strategy

Project Report – 30%
Presentation – 30%
Project Demo – 40%

Category	Required	Elective
Basic Science	06 Credits
Language	07 Credits
Mathematics	12 Credits
Core	107 Credits	16 Credits
Non-Engineering	12 Credits

PREMIER UNIVERSITY

Department of Computer Science And Engineering

PREMIER UNIVERSITY

Department of Computer Science And Engineering

CSE OBE Curriculum (Old)

Quick Links and Contacts

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Student's Link

Contact Details