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

Category-wise 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 post-graduate course) 6
IDC Interdisciplinary Course 0
  Total 60