M.Tech Curriculum
- M.Tech (2 Years)
- M.Tech R.A – July (3 Years)
- M.Tech R.A – January (3 Years)
- Elective (M. Tech)
Curriculum for M.Tech (2 Years) | ||||||
---|---|---|---|---|---|---|
Year | Odd Semester | Credits | Even Semester | Credits | ||
1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
AI5000 | Foundations of Machine Learning | 3 | AI5016 | Industry Lecture series | 1 | |
EE5609 | Matrix Theory | 3 | AI Electives | 9 | ||
CS6013 /ID2230 | Data Structures & Applications* | 3 | ||||
LA5180 | Communication Skills : Advanced | 1 | ||||
Total | 13 | Total | 13 | |||
Summer | AI6115 | Thesis Stage - I | 3 | |||
Summer Total | 3 | |||||
2 | AI6215 | Thesis stage โ II | 9 | AI6315 | Thesis stage โ III | 12 |
Total | 9 | Total | 12 |
*Either the Advanced or Regular version can be taken
Category | Credits | Percentage |
---|---|---|
Department Core | 49 | 98.00% |
LA/CA | 1 | 2.00% |
Total | 50 | 100% |
Curriculum for M.Tech R.A (July Admission) | ||||||
---|---|---|---|---|---|---|
Year | Odd Semester | Credits | Even Semester | Credits | ||
1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
AI5000 | Foundations of Machine Learning | 3 | AI5016 | Industry Lecture series | 1 | |
EE5609 | Matrix Theory | 3 | AI Electives (See Below) | 6 | ||
LA5180 | Communication Skills : Advanced | 1 | ||||
Total | 10 | Total | 10 | |||
2 | AI6115 | Thesis Stage - I | 3 | AI6215 | Thesis stage โ II | 6 |
CS6013 | Advanced Data Structures & Applications | 3 | ||||
AI Electives (See Below) | 3 | |||||
Total | 9 | Total | 6 | |||
3 | AI6315 | Thesis stage โ III | 6 | AI6415 | Thesis stage โ IV | 9 |
Total | 6 | Total | 9 |
Category | Credits | Percentage |
---|---|---|
Department Elective | 9 | 18.00% |
Department Core | 40 | 80.00% |
LA/CA | 1 | 2.00% |
Total | 50 | 100% |
Curriculum for M.Tech R.A (Jan Admission) | ||||||
---|---|---|---|---|---|---|
Year | Odd Semester | Credits | Even Semester | Credits | ||
1 | AI5030 | Probability and Stochastic Processes | 3 | AI5100 | Deep Learning | 3 |
AI5000 | Foundations of Machine Learning | 3 | EE5609 | Matrix Theory | 3 | |
AI Electives (See Below) | 3 | CS6013 | Advanced Data Structures & Applications | 3 | ||
LA5180 | Communication Skills : Advanced | 1 | AI5016 | Industry Lecture series | 1 | |
Total | 10 | Total | 10 | |||
2 | AI6115 | Thesis Stage - I | 3 | AI6215 | Thesis stage โ II | 6 |
AI Electives (See Below) | 6 | |||||
Total | 9 | Total | 6 | |||
3 | AI6315 | Thesis stage โ III | 6 | AI6415 | Thesis stage โ IV | 9 |
Total | 6 | Total | 9 |
Category | Credits | Percentage |
---|---|---|
Department Elective | 9 | 18.00% |
Department Core | 40 | 80.00% |
LA/CA | 1 | 2.00% |
Total | 50 | 100% |
Elective List for M.Tech | |
---|---|
Course Code | Course Name |
AI3102 | Sequence models |
AI5040 | Game Theory and Mechanism Design |
AI5120 | Explainability in ML |
AI5153 | Mobile Robotics |
AI5133 | AI and sensors |
AI5090 | Stochastic Processes and Applications |
AI5073 | Neuromorphic Artificial Intelligence |
AI3603/CS5290 | Computer Vision |
Reinforcement Learning | |
Convex Optimization | |
Natural Language Processing | |
Artificial Intelligence | |
Generative Artificial Intelligence | |
CS6170 | Computer Vision for Autonomous Vehicle Technology |
CS5300 | Parallel & Concurrent Programming |
CS5350 | Bayesian Data Analysis |
CS6370 | Information Retrieval |
CS5700 | Text processing and Retrieval |
CS6870 | Surveillance Video Analytics, Visual Big data analytics, Video content analysis |
CS6140 | Video Content Analysis |
CS5600 | Data Mining |
CS6460 | Visual Big Data Analytics |
CS5320 | Distributed Computing |
CS6430 | Stochastic Processes in Machine Learning |
HT5030 | Brain & Neuroscience |
EE5604 | Intro to Statistical Learning theory |
EE5605 | Kernel Methods for ML |
EE5470 | Nonlinear Control Techniques |
EE5903 | Information Theory, Coding and Inference |
EE5328 | Introduction to Submodular Functions |
EE6307 | Speech Systems |