Artificial Intelligence


Courses offered with Schedule

The Master of Technology (Online) programme in Artificial Intelligence is offered by the Division of Electrical, Electronics, and Computer Sciences. The vision of the programme is to impart rigorous training in the foundations and deep technology of Artificial Intelligence to early-career professionals with 2-8 years of experience to upskill them to become technology and business leaders in information-driven enterprises. These learnings are coupled with a unique capstone project that applies the learnings to a hands-on project relevant to the industry. Faculty from the Division of EECS, the Department of CDS, and the RBCCPS will offer contemporary courses in AI through online lectures and tutorials and will provide mentorship on capstone projects.


Program Structure

  • Core courses (14 credits): These are typically taken in the first and second semesters.
    • Random Processes (3:1)
    • Linear Algebra(3:0)
    • Linear and Non-linear Optimization (3:0)
    • Machine Learning (3:1)
  • Sample Elective courses (at least 23 credits):
    • Foundations of Robotics
    • Digital Image Processing
    • Reinforcement Learning
    • Deep Learning for Computer Vision
    • Speech Information Processing
    • Data Analytics
    • Machine Learning and Edge Computing
    • Advanced Deep Learning
    • Spectral Methods for Pattern Analysis
    • Machine Learning in Neuroscience
  • Project (27 credits): The project should be completed over three consecutive terms: Phase 1 (3 project credits), Phase 2 (12 project credits) and Phase 3 (12 project credits). The student can register for project after successfully completing four core courses.

    Students should identify an internal guide from their company who can advise them on a problem of their interest, identify the project topic and the scope of work, and monitoring the project progress. A faculty mentor will be responsible for approving the project goals and work plan, giving broad directions to the project, and bringing in the needed academic rigour for a good thesis. The faculty mentor will help conduct all project evaluations,along with additional faculty member(s)for the mid-term and final evaluations.