Program Structure [For 2023 batch onwards]

• Core courses (16 credits): These are typically taken in the first and second semesters.
  • Random Processes (3:1)
  • Linear Algebra(3:1)
  • Linear and Non-linear Optimization (3:1)
  • Machine Learning (3:1)


• Sample Elective courses (at least 21 credits):
  • Data Structures and Graph Analytics
  • Foundations of Robotics
  • Digital Image Processing
  • Reinforcement Learning
  • Deep Learning for Computer Vision
  • Data Analytics
  • Students may also take courses from any of the three streams as an elective.These are the minimum number of elective credits. More may be taken as well.



• Project (27 credits): Students can start this three-term project for 27 credits after successfully completing all the core courses. Students will complete 3 project credits in the 1st term to identify the topic, 12 credits in the 2nd term followed by a midterm evaluation, and the remaining 12 project credits the 3rd term for the final evaluation.
  
  Student will propose the topic in consultation with their Guide from within the organization, and an IISc faculty mentor will approve project goals. The faculty mentor will offer high-level feedback on the project and its progress, and coordinate the evaluations, while the in-house company Guide will offer active feedback and close support. The evaluation committee, which includes the faculty mentor and company guide, is appointed by the PCC.