Information about M.Tech in Artificial Intelligence

MTech in Artificial Intelligence

Department of Computer Science and Engineering, IIT Ropar

Starting from Academic Session - 2019-20 Sem I

 

1.

Program objectives and Purpose:

  1. The purpose is to develop a work force which can step up to the challenges of the upcoming age of artificial intelligence.
  2. Towards this end, the CS department feels the need to create a focused masters specialization along the theme
2.

Duration of the Programme :

2 years

3.

Intake of Students :

15

4.

Minimum  qualification for admission to the programme:

Candidates with B.Tech./B.E/MCA or M.Sc. in the appropriate area with valid GATE score in Computer Sc. and Information Technology (CS)

5.

National level test/Entrance Examinations :

GATE

6. 

GATE Paper Code :

CS

7.

Minimum CGPA for award of degree:

5.00 (As per Institute Norms)

8.

Total No. of credits requirement for the programme:

63  

 9.

Programme structure

Overall Structure of the Program

  • Specialization Core (21/22 credits):
    • CSE Core 14 credits  + at least 2 courses from Appendix A (i.e list of AI program core courses)
  • Elective Course credits (14 or more credits)
    • Specialization electives: At least 3 other courses from Appendix B (i.e.  list of AI program electives)
    • Department/Open Elective: One additional course i.e. 3/4 additional credits (any PG elective course). Even non-CSE PG course can be considered but with due approval from CSE RPEC and CSE HoD.
  • MTech Project (28 credits) :
    • One can register for the MTech project component only after successfully completing at least 21 course credits
    • MTech Project: Student interested in obtaining this specialization must undertake a MTech project in the area of Artificial Intelligence (or its allied areas, refer item 14)

Students will  be encouraged to take up interdisciplinary projects for fulfilling their MTP requirements. Relevance of this project with the theme of specialization would be decided by the steering committee of this specialization.

    • For the summer short semester, registration in non credited summer project or doing internship in an Industry is a mandatory component for the degree completion.

 

Max number of credits one can register in a semester = 24 

 

Semester I

Sno

Course No

Course Title

L-T-P-S-C

Credits

1

CS506

Data Structures and Algorithms

3-1-2-6-4

4

2

CS526

Mathematics for Computer Science

3-1-0-5-3

3

3

CS527

Computer Systems

3-0-2-7-4

4

4

CS509

PG Software Lab

0-0-6-6-3

3

3

XXXXX

Elective I

------

3 or 4

 

Semester II

S.no

Course No

Course Title

L-T-P-S-C

Credits

1

CSXXX

AI Program core I

------

4

2

CSXXX

AI Program core II

------

4 or 3

3

CSXXX

Elective II

------

3 or 4

4

CSXXX

Elective III

------

3 or 4

5

XXXXX

Elective IV

------

3 or 4

6

CS500

PG Seminar  

-----

0 (S/Z)

 

Summer Semester

S.no

Course No

Course Title

L-T-P-S-C

Credits

1

CSxxx

Summer Project or Industry Internship

-----

 0 (S/Z)

 

Semester III

S.no

Course No

Course Title

L-T-P-S-C

Credits

1

XXXX

Colloquium Series  (approximately 1 hr/week)

-----

 0 (S/Z)

2

CS698

Project-1

0-0-24-12-12

12

 

Semester IV

S.no

Course No

Course Title

L-T-P-S-C

Credits

1

CS500

PG Seminar  (Topics specific to one’s research Project)

-----

0 (S/Z)

2

CS699

Project-2

0-0-32-16-16

16

 

  14.

Area of Research for Internship/ Project :

Core topics in Artificial Intelligence (AI), Machine Learning (ML) and Data Mining (DM). Applications of AI/ML/DM in areas such as Internet of Things, Computer Vision and Image processing are also included.

Appendix A- List of AI Program Core (at least 2 courses from this list)

Sno

Course No

Course Title

L-T-P-S-C

Credits

1

CS503

Machine Learning

3-0-2-7-4

4

2

CS512

Artificial Intelligence

3-0-2-7-4

4

3

CS521

Fundamentals of Data Sciences

3-0-2-7-4

4

4

CS524

Data Mining

3-0-2-7-4

4

5

CS504

Artificial Neural Networks (Deep Learning)

3-0-0-6-3

3

* In general, as new courses are designed and floated, this list would adapt with time.

 

Appendix B- List of AI Program Electives (at least 3 course from this list)

Sno

Course No

Course Title

L-T-P-S-C

Credits

1

CS503

Machine Learning

3-0-2-7-4

4

2

CS512

Artificial Intelligence

3-0-2-7-4

4

3

CS521

Fundamentals of Data Sciences

3-0-2-7-4

4

4

CS524

Data Mining

3-0-2-7-4

4

5

CS504

Artificial Neural Networks

3-0-0-6-3

3

6

CS507

Multimedia Systems

2-0-4-6-4

4

7

CS517

Digital Image Processing and Analysis

3-0-2-7-4

4

8

CS518

Computer Vision

3-0-2-7-4

4

9

CS522

Social Networks

3-0-2-7-4

4

10

CS530

Multi Agent Systems

2-0-2-6-3

3

11

CS532

Security Analytics

2-0-2-6-3

3

12

CS533

Reinforcement Learning

2-0-2-6-3

3

13

CS609

Network Science

3-0-2-7-4

4

14

CS612

Advanced Machine Learning

2-0-4-6-4

4

15

CS615

Biomedical Image Processing & Analysis

3-0-2-7-4

4

16

CS616

Advanced Computer Vision

3-0-2-7-4

4

17

CS617

Affective Computing and Interaction

3-0-2-7-4

4

18

CS620

Introduction to Spatial Computing

3-0-2-7-4

4

19

CS621

Probabilistic Graphical Models

3-0-2-7-4

4

20

CS622

Advanced Image Processing

3-0-2-7- 4

4

21

CS701

Special Topics in Complex Networks

3-0-2-7-4

4

22

CS702

Special Topics in Social Computing

3-0-2-7-4

4

23

CS724

Advanced Data Mining

3-0-2-7-4

4

** In general, as new courses are designed and floated, this list would adapt with time.