Course and Credit Details

General Details

  1. There are five discipline core courses. The remaining courses (excluding the open electives) are grouped into three specialization baskets.
  2. It is mandatory to take one course from each basket with minimum 9 credits.
  3. Students who would like to go for a specialization must take at least 9 more credits (in addition to the mandatory credits in Point 2) from whichever basket they want to specialize in.
  4. Those who do not want any specialization can take courses to satisfy 9 credits from across the baskets.
  5. The post graduate project is of one year duration.
  6. Post graduate projects should be industry sponsored.
  7. Post graduate project starts from the summer following the first year and extends to the third and fourth semesters.
  8. Students who would like to go for a specialization must have their post graduate project in the same basket.
  9. Systems Design should be taken during the winter break after the first semester.

Minimum credit requirements

Course typeCredits
Discipline core (DC)15
Specialization basket (SB)$ 18*
Systems Design 2
Post graduate project 28
Outside discipline electives (OE)^ 6
Technical communication 1
Total 70

$ Out of these 18 credits, at least 9 credits must be earned by taking one course each from the 3 baskets. At least 9 credits should be chosen from the same basket (if a specialization is desired) or from across baskets (if no specialization is desired.)

^ Any graduate level course outside of the Communication and Signal Processing discipline from the school or from other schools is acceptable as outside discipline elective.

List of Core courses for M.Tech. in Communications and Signal Processing Program

(Total Credits for discipline core = 15)
Sr.No. Code Course title Lecture(L) Tutorial(T) Practicals(P) Credits(C) Semester
3 CS571 Programming Practicum1033I
2 EE522 Matrix Theory for Engineers3003I
4 EE530 Applied Optimization3003II
1 EE534 Probability and Random Processes3003I
5 EE536 IoT Systems2023II

Semester wise distribution of all courses

I year, Semester 1
CodeCourse titleCreditsRemarks
CS571Programming Practicum3DC
EE522Matrix Theory for Engineers3DC
EE534Probability and Random Processes3DC
-Specialization Basket3SB
-Specialization Basket3SB
HS541Technical Communication1
Total credits16
I year, Winter break
CodeCourse titleCreditsRemarks
EE532Systems Design2
I year, Semester 2
CodeCourse titleCreditsRemarks
EE530Applied Optimization3DC
EE536IoT Systems3DC
-Specialization Basket3SB
-Specialization Basket3SB
-Specialization Basket3SB
-Outside discipline electives3OE
Total credits18
II year, Semester 3
CodeCourse titleCreditsRemarks
EE626PPost Graduate Project10
-Specialization Basket3SB
-Outside discipline electives3OE
Total credits16
II year, Semester 4
CodeCourse titleCreditsRemarks
EE627PPost Graduate Project18

List of courses in specialization baskets

Signal processing basket
CodeCourse titleL-T-P-C
CS609Speech Processing3-0-2-4
EE529Embedded Systems3-0-2-4
EE541Tensors: Techniques, Algorithms, Applications for Signal Processing, and Machine Learning3-0-2-4
EE608Digital Image Processing3-0-2-4
EE620Advanced Digital Signal Processing3-0-0-3
Communication basket
CodeCourse titleL-T-P-C
CS549Performance Analysis of Computer Networks3-0-0-3
EE503Advanced Communication Theory3-0-0-3
EE507Transmission lines and Basic Microwave engineering3-1-0-4
EE517Wireless Communication and Networks3-0-0-3
EE518Information Theory3-0-0-3
EE529Embedded Systems3-0-2-4
EE531Estimation and Detection Theory3-0-0-3
EE541Tensors: Techniques, Algorithms, Applications for Signal Processing, and Machine Learning3-0-2-4
EE580Network Systems: Modelling and Analysis3-0-0-3
EE621Radiating Systems3-1-0-4
EE641Advanced Wireless Technologies3-0-0-3
Machine Learning basket
CodeCourse titleL-T-P-C
CS669Pattern Recognition3-1-0-4
CS671Deep Learning and Applications3-0-2-4
CS672Advanced Topics in Deep Learning3-0-2-4
EE511Computer Vision3-1-0-4
EE531Estimation and Detection Theory3-0-0-3
EE541Tensors: Techniques, Algorithms, Applications for Signal Processing, and Machine Learning3-0-2-4