M Tech Programme – Signal Processing Curriculum (from 2022 -2024 batch onward)

FOUNDATIONAL COURSES
(Compulsory; Minimum 19 credits)

• E1 244 3:0 Detection and Estimation Theory [JAN]
• E2 202 3:0 Random Processes [AUG]
• E1 251 3:0 Linear and Nonlinear Optimization [AUG]
• E2 212 3:0 Matrix Theory [AUG]
  ————–or E0 298 3:1 Linear Algebra And Its Applications [AUG].
• E1 213 3:1 Pattern Recognition and Neural Networks [JAN]
  ————–or E0 270 3:1 Machine Learning [JAN]
  ————–or E2 236 3:1 Foundations of Machine Learning [JAN]
  ————–or E9:205 3:1 Machine Learning for Signal Processing [JAN]
• E9 222 0:3 Signal Processing in Practice [JAN]

SPECIALIZATION MODULES
(Selecting a module is compulsory; Minimum 9 credits from the selected module)

Speech and Language Processing Module

• E9 261 3:1 Speech Information Processing
• E9 211 3:0 Adaptive Signal Processing
• E9 213 3:0 Time-Frequency Analysis
• E9 203 3:0 Compressed Sensing and Sparse Signal Processing
• E0 334 3:1 Deep Learning for Natural Language Processing
• E9 309 3:1 Advanced Deep Learning
• E1 246 3:1 Natural Language Understanding

Image, Video, and Computer Vision Module

• E9 213 2:1 Digital Image Processing
• E9 246 3:1 Advanced Image Processing
• E9 208 3:1 Digital Video: Perception and Algorithms
• E1 216 3:1 Computer Vision
• E9 310 3:1  Computational Imaging
• E9 245 3:0 Selected Topics in Computer Vision
• DS 265 3:1 Deep Learning for Computer Vision

Communication Module

• E2 201 3:0 Information Theory
• E2 211 3:0 Digital Communication
• E9 203 3:0 Compressed Sensing and Sparse Signal Processing
• E2 203 3:0 Wireless Communications
• E9 231 3:0 MIMO Signal Processing
• E9 211 3:0 Adaptive Signal Processing
• E2 251 3:0 Communication System Design
• E9 271 3:0 Space-Time Signal Processing and Coding

Learning Module

• E1 245 3:0 Online Prediction and Learning
• E0 350 3:1 Advanced Convex Optimization
• E9 309 3:1 Advanced Deep Learning
• E9 203 3:0 Compressed Sensing and Sparse Signal Processing
• E9 333  3:1   Advanced Deep Representation Learning
• E0 268 3:1 Practical Data Science
• E0 259 3:1 Data Analytics
• E0 306 3:1 Deep Learning: Theory and Practice
• E1 260 3:1 Optimization for Machine Learning and Data Science

Power Module

• E4 234 3:0  Advanced Power Systems Analysis
• E4 221 2:1  DSP and AI Techniques in Power System Protection
• E4 231 3:0 Power System Dynamics and Control
• E4 233 3:0 Computer Control of Power Systems
• E9 213 3:0 Time-Frequency Analysis
• E9 201 3:0 Digital Signal Processing
• E9 291 2:1 DSP System Design

Electives may also be chosen outside of those listed above, from the vast array of courses offered in the Institute regardless of which department offers them, with prior permission from the Faculty Advisor.

Project

•     SP 299 0:28  MTech Project