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)
Specialization modules for the MTech (SP) program for the 25-27 batch.
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
E9 201 3:0 Digital Signal Processing
DS 207 3:1 Introduction to Natural Language Processing
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
E2 237 3:0 Statistical Learning Theory
DS 246 1:2 Generative and Agentic AI in Practice
E0 334 3:1 Deep Learning for Natural Language Processing
DS 215 3:0 Introduction to Data Science
E1 240 3:0 Theory of Multi-Armed Bandits
E0 319 3:0 Learning-theoretic foundations of modern machine learning
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
DS 261 3:1 Artificial Intelligence for Medical Image Analysis
E9 247 3:1 Learning for 3D Vision and Inverse Graphics
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
E2 217 3:1 Machine learning for Wireless Communication
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.
