With an objective to align with National Education Policy (NEP) 2020 of India, the Department of Computer Science has decided to offer one year Master in Science (M.Sc.) programme in Computer Science. The programme also provides an option to specialize in the area of Artificial Intelligence & Machine Learning.
MINIMUM ELIGIBILITY CRITERIA
A candidate must have passed Bachelor’s degree in Computer Science or a closely related discipline* with Mathematics as a subject either at the Bachelor’s level or at the 10+2 (12th class) level under 10+2+4 pattern from a recognized institution or an examination recognized by the University as its equivalent with a minimum of 55% marks (or an equivalent grade).
*Indicative List of Closely Related Disciplines
Computer Science and Engineering/ Computer Engineering/ Computer Applications/ Information Technology
SPECIALIZATION
The programme offers an option to students for acquiring skills and excellence in the area of Artificial Intelligence & Machine Learning
The students can either choose to get the respective degree with specialization or without specialization (General Track).
The students admitted in the programme are required to submit their choice of degree with specialization or degree without specialization before the beginning of the first semester.
The placement of the one year M.Sc. in all masters programmes offered by the department is shown below in figure 1.
Exit (for EL-1, 4 years degree) with one year M.Sc. degree in Computer Science with or without specialization
CATEGORIZATION OF COURSES
The following categories of courses will be taught in the Master’s programmes:
Core Courses: These are core courses that will be compulsorily studied by the students as a core requirement to complete the requirements of the respective degree.
Track Elective (TE) Courses: These are optional courses offered in the areas of specialization.
General Elective (GE) Courses: These are optional courses not related to any track.
Open Elective (OE) Courses: These are the relevant optional courses that can be chosen from the other departments in the university.
PROGRAMME STRUCTURE
The programme structure along with credit distribution of courses is summarized in Table 1 and 2.
TABLE 1: One Year M.SC. (COMPUTER SCIENCE) PROGRAMME STRUCTURE
|
Semester |
Course Type |
Course Title |
Code |
L-T-P |
Credits |
|
1st |
Core |
Fundamentals of Artificial Intelligence |
CS 502 |
3-1-0 |
4 |
|
Core |
Fundamentals of Machine Learning |
CS 503 |
3-0-2 |
4 |
|
|
Core |
Optimization Techniques |
CS 506 |
3-1-0 |
4 |
|
|
Core |
Advanced Data Structure and Algorithms |
CS 507 |
3-0-2 |
4 |
|
|
– |
Minor Project/Dissertation (Part I)* |
CS 508 |
– |
4 |
|
|
2nd |
Core |
Introduction to South Asia |
– |
2-0-0 |
2 |
|
OE |
Open Elective |
– |
2-0-0 |
2 |
|
|
TE |
Track Elective 1 |
– |
– |
4 |
|
|
TE |
Track Elective 2 |
– |
– |
4 |
|
|
– |
Major Project/Dissertation (Part II) |
CS 509 |
– |
12 |
|
|
Total |
44 |
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|
Note: *The students opting for project have to submit Project Report at the end of both 1st semester and final project report at the end of 2nd semester. The project can be done in industry or under the supervision of faculty of the department. The students opting for dissertation have to present the work done at the end of 1st semester and 2nd semester both. The dissertation needs to be submitted at the end of 2nd semester. The students opting for specialization have to choose dissertation/project in the area of specialization. |
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TABLE 4: TE AND GE COURSES
|
Course Type |
Course Title |
Course Code |
L-T-P |
Credits |
|
Track 1: Artificial Intelligence & Machine Learning |
||||
|
TE |
Advanced Machine Learning |
CS-E 501 |
3-1-0 |
4 |
|
TE |
AI and ML Techniques for Cyber Security |
CS-E 502 |
3-0-2 |
4 |
|
TE |
Big Data Analytics |
CS-E 503 |
3-1-0 |
4 |
|
TE |
Computational Intelligence |
CS-E 504 |
3-0-2 |
4 |
|
TE |
Deep Learning |
CS-E 505 |
3-0-2 |
4 |
|
TE |
Evolutionary Algorithms |
CS-E 506 |
3-1-0 |
4 |
|
TE |
Information Retrieval |
CS-E 507 |
3-1-0 |
4 |
|
TE |
Natural Language Processing |
CS-E 508 |
3-1-0 |
4 |
|
TE |
Network Science |
CS-E-509 |
3-0-2 |
4 |
|
TE |
Reinforcement Learning |
CS-E-510 |
3-0-2 |
4 |
|
TE |
Social Media Analytics |
CS-E 511 |
3-0-2 |
4 |
|
Track 2: Advanced Network & Systems |
||||
|
TE |
Blockchain Technology |
CS-E 521 |
3-1-0 |
4 |
|
TE |
Cloud Computing |
CS-E 522 |
3-1-0 |
4 |
|
TE |
Cryptography and Network Security |
CS-E 523 |
3-1-0 |
4 |
|
TE |
Distributed Systems |
CS-E 524 |
3-1-0 |
4 |
|
TE |
Internet of Things |
CS-E 525 |
3-1-0 |
4 |
|
TE |
Linear Programming for Computer Networks |
CS-E 526 |
3-1-0 |
4 |
|
TE |
Optical Networks |
CS-E 527 |
3-1-0 |
4 |
|
TE |
Performance Modeling of Computer Networks |
CS-E-528 |
3-1-0 |
4 |
|
TE |
Software Defined Networking |
CS-E-529 |
3-1-0 |
4 |
|
General Electives |
||||
|
GE |
Distributed Machine Learning |
CS-E 541 |
3-1-0 |
4 |
|
GE |
Embedded Systems Design |
CS-E 542 |
3-1-0 |
4 |
|
GE |
Fuzzy Modelling |
CS-E 543 |
3-1-0 |
4 |
|
GE |
Logic for Computer Science |
CS-E 544 |
3-1-0 |
4 |
|
GE |
Mobile Computing |
CS-E 545 |
3-1-0 |
4 |
|
GE |
Performance Modeling and Simulation of Computer Systems |
CS-E 546 |
3-1-0 |
4 |
|
GE |
Queueing Theory with Applications |
CS-E 547 |
3-0-2 |
4 |
|
GE |
Real-Time Systems |
CS-E-548 |
3-1-0 |
4 |
|
GE |
Soft Computing |
CS-E-549 |
3-1-0 |
4 |
|
GE |
Theory of Computation |
CS-E-550 |
3-1-0 |
4 |
|
Remark: A student opting for general track can take courses from any specialization track. Besides above-listed courses, new TE or GE courses may be offered after approval of the BoS. |
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