Curriculum overview
First semester curriculum is designed with the above diversity in mind. Apart from the common
/courses, M.Sc. Mathematics students will be trained in certain basic core /courses from computer
science and B.Tech. Computer Science and Engineering students will be trained in some core
mathematics /courses. Later semesters will comprise of a wide spectrum of advanced /courses in
both the domains. Major areas include Algorithms, Graph Theory, Combinatorics, Logic,
Computational Methods and Foundations of Data Science & Machine learning. The program
culminates with an year long Project/Dissertation in the second year, that prepares students to
pursue careers that require innovations involving sophisticated applications of mathematics in
computer science.
Core /courses (Theory): Linear Algebra, Probability and Statistics, Topics
in Discrete Mathematics, Algorithms, Theory of Computation, Graph Theory and
Combinatorics, Foundations of Data Science and Machine Learning.
Core /courses (Lab): Programming Lab, Computational Methods and Applications.
Details

Semester I: Total credits is 18. If a student has already credited a course with a similar content as some core course
prescribed in this curriculum, during his/her previous degree, then a program elective course may
be credited instead of that course, for completing the credit requirements. For this, the permission
of the faculty advisor is to be obtained.

Semester II: Total credits is 16.

Semester III: Total credits is 14. The students are free to take Open Electives either from the set of Program Electives or from the set of any research or PG level electives in the institute.

Semester IV: Total credits is 12. Minimum credit requirements is 58 credits
Semester I (For students with Computer Science and Engineering background)
Code 
Course Title 
Category 
CS5013 
Topics in Discrete Mathematics 
PMT 
CS5009 
Algorithms 
PMT 
MA5007 
Probability and Statistics 
PMT 
MA5001 
Linear Algebra 
PMT 
CS5107 
Programming Lab 
PML 
GN5001 
Communication and Technical Writing Skills 
IDC 
Semester I (For students with Mathematics background)
Code 
Course Title 
Category 
CS5013 
Topics in Discrete Mathematics 
PMT 
CS5009 
Algorithms 
PMT 
MA5007 
Probability and Statistics 
PMT 
CS5017 
Theory of Computation 
PMT 
CS5107 
Programming Lab 
PML 
GN5001 
Communication and Technical Writing Skills 
IDC 
Semester II
Code 
Course Title 
Category 
CS5016 
Computational Methods and Applications 
PMT 
CS5010 
Graph Theory and Combinatorics 
PMT 
CS5014 
Foundations of Data Science and Machine Learning 
PMT 

Program Major Elective 
PME 

Open Elective 
Semester III
Code 
Course Title 
Category 

Program Major Elective 
PME 

Open Elective 
OE 

Project / Dissertation Phase 1 
PMP 
Semester IV
Code 
Course Title 
Category 

Project / Dissertation Phase 2 
PMP 
Categorywise Summary
Code 
Category Description 
Credits 
PMT 
Program Major Theory (Lecture based core /courses) 
25 (Minimum 23) 
PML 
Program Major Lab (Lab based core /courses) 
3 
PMP 
Program Major Project (Project/Internship based core /courses) 
20 
PME 
Program Major Elective (Electives /courses from program pool) 
6 
OE 
Open Electives (Any postgraduate course) 
6 
IDC 
Interdisciplinary Course 
0 

Total 
60 