Doctoral
PhD Curriculum
The PhD program involves coursework tailored to the student’s research area, followed by intensive research culminating in a dissertation.
Tracks offered include Direct PhD and Regular/External PhD.
Electives can be chosen in consultation with the research advisor.
Curriculum for Direct PhD
| Course Code | Course Name | Credits |
|---|---|---|
| AI5000 | Foundations of Machine Learning | 3 |
| AI5030 | Probability and Stochastic Processes | 3 |
| AI5100 | Deep Learning | 3 |
| EE5609 | Matrix Theory | 3 |
| CS6013 /ID2230 | Advanced Data Structures and Algorithms / Data Structures & Applications | 3 |
| AI Electives (See List Below) | 9 | |
| Total | 24 |
Elective List for Direct PhD
| 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 |
Curriculum for Regular/External PhD
| Course Code | Course Name | Credits |
|---|---|---|
| AI5000 | Foundations of Machine Learning | 3 |
| CS6013 /ID2230 | Advanced Data Structures and Algorithms / Data Structures & Applications | 4 |
| CS201 | AI Electives (See list below) | 6 |
| Total | 13 |
Elective List for Direct PhD
| 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 |