MS in Computer Science and Engineering

Course Work 

On joining the Institute every student is required to plan his/her course-work in consultation with a Faculty Advisor.

Credit Requirements 
  1. All students of the MS programme are normally required to complete the prescribed 34 credits within the first two semesters from the date of joining, by completing the coursework prescribed by the faculty advisor. 

  2. In addition, the research scholars should complete PP/NP course on Communication Skills.

  3. MS students will be allowed to take additional courses beyond the prescribed 34 credits, with the approval of APEC. 

  4. MS students will be allowed to take only one UG course for credit requirements.                                                                                                                         Additional details on course work can be found in MS-R.4.1 in MS Rule Book 

Performance Requirements 

A student MUST get at least CC grade in EVERY course (other than optional) registered as a credit course, including seminar. 
Academic Probation to the students having lower grade than the minimum requirement for continuation of their studies may be given. For students who have scored grade lower than CC in at most one course in their first semester may be offered an academic probation, with appropriate conditions decided by APEC.

Syllabus for MS in CSE

List of PG Courses from CSE Departments

S.No

Course Code

Name of Course

L-T-P-C

1CS 601Software Development for Scientific Computing3-0-0-6
2CS 603Approximation algorithms3-0-0-6
3CS 604Parametrized Algorithms and Complexity3-0-0-6
4CS 605Reinforcement Learning3-0-0-6
5CS 606Advanced Topics in Embedded Computing3-0-0-6
6CS 607Advanced Computer Networks3-0-0-6
7CS 608FPGA for communication networks prototyping3-0-0-6
8CS 609Software Defined Networking (SDN) and Network Function Virtualization (NFV)3-0-0-6
9CS 610Advanced Distributed Systems3-0-0-6
10CS 611Advanced Software Systems Lab0-1-6-8
11CS 612Statistical Pattern Recognition Laboratory0-0-3-3
12CS 614Reinforcement Learning Laboratory0-0-3-3
13CS 616Statistical Pattern Recognition3-0-0-6
14CS 617Special Topics in Hardware Systems3-0-0-6
15CS 620Formal Models for Concurrent and Asynchronous Systems3-0-0-6
16CS 621Logic and Applications3-0-0-6
17CS 622Special Topics in Automata and Logics3-0-0-6
18CS 623Advanced Topics in Communication Networks3-0-0-6
19CS 624Compilers - Principles and Implementation3-0-0-6
20CS 625Topics in Stochastic Control and Reinforcement Learning3-0-2-8
21CS 626Topics in Data Structures and Algorithms2-0-2-6
22CS 627Data Structures3-0-0-6
23CS 628Algorithms3-0-0-3
24CS 629Introduction to Reinforcement Learning2-0-2-6
25CS 630Statistical Machine Learning2-0-2-6
26CS 631Seminar0-0-4-4
27CS 632Runtime Verification3-0-0-6
28CS 702Systems Bootcamp for ML1-0-2-4
29CS 703Topics in Design and Analysis of Algorithms3-0-0-6
30CS 704Advanced Algorithms3-0-0-6
31CS 705Topics in Graph Theory3-0-0-6
32CS 706Topics in Parameterized Algorithms and Complexity3-0-0-6
33CS 801Power Aware Computing3-0-2-8
34CS 802Dataflow Processor Architecture3-0-0-6
35CS 810Advanced Computer Architecture3-0-3-9