AI | Centre For Continuing Education, IISc

Source: https://cce.iisc.ac.in/iken/mtech-online/ai/ Parent: https://cce.iisc.ac.in/iken/mtech-online/

Artificial Intelligence

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 [For 2025 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 20 credits):

• Data Analytics
• Reinforcement Learning
• Data Structures and Graph Analytics
• Edge and Cloud Systems for Machine Learning Algorithms
• Deep Generative Models
• Introduction to Cryptography

• Deep Learning for Robotics

• Project (28 credits): Students can start this three-term project for 28 credits after successfully completing all the core courses. Students will complete 4 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.

Program Structure [For 2023 and 2024 batch]

• 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): See sample courses above

• 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.

Program Structure [For 2021 and 2022 batch]

• 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): See sample courses above

• • Project (27 credits): See instructions for project above