Centre for Continuing Education · IISc Bangalore
Artificial Intelligence
Organisations that have entered into a Memorandum of Agreement with IISc, enabling their employees to enrol in the M.Tech. (Online) programme.
M.Tech (Online) · Stream
Artificial Intelligence
Offered by the Division of Electrical, Electronics and Computer Sciences (EECS), this programme imparts rigorous training in the foundations and deep technology of AI for early-career professionals with 2–8 years of experience.
Faculty from EECS, the Department of CDS, and RBCCPS offer contemporary AI courses through online lectures and tutorials, and provide mentorship on capstone projects.
Programme at a Glance
Total Programme Credits
Core Course Credits
Min Elective Credits
Project Credits
Foundation Courses
Core Courses — 16 Credits
Four mandatory core courses typically taken in the first and second semesters. These build the mathematical and computational foundations for all AI electives and the project.
Random Processes
Probability theory, stochastic processes, Markov chains, and their applications in AI and signal modelling.
Linear Algebra
Matrices, vector spaces, eigenvalues, singular value decomposition — the language of machine learning and data science.
Linear & Non-linear Optimization
Convex optimisation, gradient descent, constrained and unconstrained methods powering modern AI training algorithms.
Machine Learning
Supervised, unsupervised, and semi-supervised learning — the central discipline of the AI stream.
Specialisation Courses
Sample Elective Courses — Min 20 Credits
Choose from 30+ electives offered across the M.Tech. (Online) programme. Sample electives for the AI stream include:
Data Analytics
Statistical methods, data wrangling, visualisation, and exploratory analysis applied to real-world datasets.
Reinforcement Learning
Agents, environments, reward signals, Q-learning, policy gradients, and deep RL for sequential decision-making.
Data Structures & Graph Analytics
Algorithms, graph representations, network analysis, and graph neural networks for AI applications.
Edge & Cloud Systems for ML
Deploying and scaling ML algorithms across edge devices and cloud infrastructure for real-time inference.
Deep Generative Models
VAEs, GANs, diffusion models, and their applications in generating images, text, and structured data.
Deep Learning for Robotics
Perception, planning, and control for autonomous systems using deep neural networks and sensor fusion.
Introduction to Cryptography
Foundations of cryptographic systems, security protocols, and their relevance to AI data privacy and secure computation.
Capstone Work
Project — 28 Credits across 3 Phases
Students may begin the project only after successfully completing all core course credits. The project runs over three consecutive terms, each with a defined deliverable and evaluation milestone.
Phase 1 — Topic & Setup
Identify the project topic in consultation with the company guide. Finalise and get IISc faculty mentor approval for project goals, work plan, and scope.
Phase 2 — Development
Core project execution. Ends with a mid-term evaluation by a committee consisting of the faculty mentor, company guide, and one additional PCC-nominated faculty member.
Phase 3 — Completion
Thesis write-up and final evaluation. No course credits allowed in this term. All course requirements must be completed before starting Phase 3.
IISc Faculty Mentor
Approves project goals, offers high-level technical feedback and academic direction, and coordinates mid-term and final evaluations.
Company Guide
Provides active feedback and close support on the in-house project work. Part of the evaluation committee appointed by the PCC.
Programme History
Structure by Batch
The programme structure has been refined over each admission cycle. Select your batch below to see the applicable credit requirements.
2025 Batch Onwards — Current Structure
Core Courses — 16 Credits
- Random Processes (3:1)
- Linear Algebra (3:1)
- Linear and Non-linear Optimization (3:1)
- Machine Learning (3:1)
Elective Courses — Minimum 20 Credits
Chosen from 30+ M.Tech. (Online) elective courses. See sample electives above.
Project — 28 Credits
3-phase project: 4 credits (Phase 1) + 12 credits (Phase 2, mid-term eval) + 12 credits (Phase 3, final eval).
2023 & 2024 Batch
Core Courses — 16 Credits
- Random Processes (3:1)
- Linear Algebra (3:1)
- Linear and Non-linear Optimization (3:1)
- Machine Learning (3:1)
Elective Courses — Minimum 21 Credits
Chosen from available elective offerings for the relevant year.
Project — 27 Credits
3-phase project: 3 credits (Phase 1) + 12 credits (Phase 2, mid-term eval) + 12 credits (Phase 3, final eval).
2021 & 2022 Batch
Core Courses — 14 Credits
- Random Processes (3:1)
- Linear Algebra (3:0)
- Linear and Non-linear Optimization (3:0)
- Machine Learning (3:1)
Elective Courses — Minimum 23 Credits
Chosen from available elective offerings for the relevant year.
Project — 27 Credits
3-phase project as described in the Project Guidelines.
Apply Now
Interested in the AI Stream?
Admissions for the 2026 batch are now open. Ask your organisation to nominate you — or check if your company already has an MoA with IISc.