PhD


PhD in AI Curriculum (Jan 2021 onwards)

  • MHRD PhD students may decide the supervisor at the time of joining or by the end of first semester. For a research project-funded position, the faculty executing that research project himself/herself will be the guide.
  • Phd students have to maintain a minimum CGPA of 7 from their course work.
  • PhD studnets are required to give a comprehensive exam within 12 months of joining (maximum 2 chances to pass comprehensive exam).
  • After passing the comprehensive exam, the student has to present the research proposal seminar (RPS) within 3 months. RPS for regular and direct PhD students should be within 18 months of registration.
  • The guide will constitute a Doctoral Committee (DC), and DC would conduct regular meetings to evaluate the progress of the work.
  • Following syllabus holds for part time PhD as well
  • Electives not in given lists can be considered with approval of faculty advisor

PhD candidates joining after M.Tech or Equivalent degree

  • Need to complete 12 credits of coursework in 1 year with 6 credits of mandatory courses (AI5000 and ID2230/CS6013).
  • 6 credits of electives can be any course from the elective basket.
Curriculum (first year)Credits
AI5000Basics/Foundations of Machine Learning3 (Core)
ID2230/CS6013Advanced Data Structures and Algorithms3 (Core)
XXxxxxElectives (basket below)6
Total12


PhD candidates joining after BTech/BE/MSc/Equivalent Degree in any discipline (aka Direct PhD candidates)

  • Need to complete 24 credits of coursework in 1 year with 6 credits of mandatory courses (AI5000 and ID2230/CS6013).
Curriculum (first year)Credits
AI5000Basics/Foundations of Machine Learning3
AI5030Probability and Stochastic Processes3
AI5100Deep Learning3
EE5609Matrix Theory3
ID2230/CS6013Advanced Data Structures and Algorithms3
XXxxxxElectives (baskets below)10
Total24


AI Electives

CourseCredits
Intro to Statistical Learning Theory1
Kernel Methods1
Sequence Models1
Bayesian Data Analysis1
Non-linear Control Techniques1
Optimisation Methods in Machine Learning/Convex Optimization3
Information Theory and Coding3
Stochastic Processes for Machine Learning1
Introduction to Submodular Functions1
Artificial Intelligence2
CourseCredits
Information Retrieval3
Natural Language processing3
Data Mining3
Text Processing3
Computer Vision3
Speech Systems3
Image and Video Processing3
Surveillance Video Analytics, Visual Big data analytics, Video content analysis3
Computer Vision for Autonomous Vehicle Technology3
Parallel & Concurrent Programming3
Distributed Computing3
Please find latest Academics handbook here