Generative AI & Deep Learning — Shared Course Resources · Dr. Prathosh A.P., IISc Bengaluru
A comprehensive reference covering probabilistic graphical models, deep generative models, Bayesian deep learning, and advanced inference methods. Freely available online at probml.github.io ↗
In August 2026, the same core material is being offered in three parallel formats: as a 12-week NPTEL online course (open globally), as an in-person course at IISc (ADRL), and as an online distance-learning course for IISc MTech students. All three variants share identical lecture notes, reading lists, and core materials, ensuring consistency across platforms.
The course covers the mathematical and algorithmic foundations of modern generative models — from classical probabilistic ML and Bayesian inference through to state-of-the-art deep generative architectures including VAEs, GANs, normalising flows, diffusion models, and large language models.
The material draws on the instructor's active research programme in generative AI (machine unlearning, symmetry discovery, neural operators, and generative AI for medicine) and is designed to give participants both theoretical depth and practical intuition regardless of which platform they access the course through.