Metadata
Title
ONLINE COURSE ON GENERATIVE AI
Category
undergraduate
UUID
b182ac479bf148b9b6197bd878e77efc
Source URL
https://cce.iisc.ac.in/cce-proficience/generative-ai-principles-and-applications...
Parent URL
https://cce.iisc.ac.in/cce-proficience/
Crawl Time
2026-03-23T22:39:04+00:00
Rendered Raw Markdown

ONLINE COURSE ON GENERATIVE AI

Source: https://cce.iisc.ac.in/cce-proficience/generative-ai-principles-and-applications-mj-2026/ Parent: https://cce.iisc.ac.in/cce-proficience/

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CCE-PROFICIENCE MAY – JULY 2026

Duration

3 months May -July 2026

Schedule

Every Saturday

Saturdays 10 A.M. to 1 P.M.

Course offered

Online

Exam Duration

31 July to 9 August 2026

Classes Start

~4 May 2026

Objectives of the Course

This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Students will learn about various types of deep generative models such as variational autoencoders, generative adversarial networks, autoregressive models, Diffusion Models and Large Language Models and RLHF. The course will cover both mathematical foundations and practical implementations of these models using popular frameworks like PyTorch. Students will gain hands-on experience through lectures and assignments, allowing them to explore deep generative models across various Al tasks.

Syllabus

This course covers Probabilistic Deep Generative Modelling, including Variational Divergence Minimization. It explores Generative Adversarial Networks (GANS, WGANs), Variational Autoencoders (VAES, VQVAE), and Denoising Diffusion Probabilistic Models (DDPMs). The curriculum also delves into Conditional Diffusion, Score-based models, and Large Language Models (LLMs), focusing on sampling, quantization, and reinforcement learning-based alignment methods like PPO and DPO.

Minimum Qualification required by the candidates

Basic undergraduate degree, Basic programming skills in Python,

Anyone who would like to understand the nuances of genAl from a mathematical perspective.

Reference Books

  1. Kevin P. Murphy”, “Probabilistic Machine Learning: Advanced Topics”, “MIT Press”, 2023
  2. Deep Generative Models, Jakub M. Tomczak, Springer 2024.
  3. Foster D. Generative deep learning. ” O’Reilly Media, Inc.; 2023

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Know The Facilitators

Dr. Prathosh A P

Assistant Professor

Electrical Communication Engineering

Indian Institute of Science

Course Fee

Particulars Amount
Course Fee 15,000
Application Fee 300
GST@18% 2,754
Total 18,054