Probabilistic Machine Learning
CS772A
2025-26 (even semester)
Class Timings: Mon/Wed 18:00 - 19:15
Class Venue: RM-101
Instructor: Piyush Rai (Office RM-502, email: piyush AT cse DOT iitk DOT ac DOT in)
Office Hours: By appointment (RM-502)
Q/A Forum: Piazza

Background and Course Description

Probabilistic models for data are ubiquitous in many areas of science and engineering, and specific domains such as visual and language understanding, finance, healthcare, biology, climate informatics, etc. This course will be an advanced introduction to probabilistic models of data (often through case studies from these domains) and a deep-dive into advanced inference and optimization methods used to learn such probabilistic models. This is an advanced course and ideally suited for student who are doing research in this area or are interested in doing research in this area.

Pre-requisites

Instructor's consent. The course expects students to have a strong prior background in machine learning (ideally through formal coursework, such as CS771), and ideally also some prior exposure/appreciation to basic principles of probabilistic modeling. The students are expected to have strong foundations in probability and statistics, linear algebra, and optimization, and must also be proficient in programming in Python.

Grading

There will be 2 homeworks (30%), 2 quizzes (20%), a mid-sem exam (20%), and an end-sem exam (30%).

Reference materials

There will not be any dedicated textbook for this course. We will use lecture slides/notes, monographs, tutorials, and papers for the topics that will be covered in this course. Some recommended, although not required, reference books are listed below:

Schedule

Lec. No. Topics Readings/References/Comments Slides/Notes
1 Course Logistics, Intro to Probabilistic Machine Learning A review article on PML and AI, Probability and statistics refresher slides PPTX slides, PDF slides
2 Getting Started: PML Basics PML-1 (Section 4.6), Optional/recommended: Chapter 2 and Appendix B of PRML PPTX slides, PDF slides
3 Parameter Estimation and Prediction in Probabilistic Models PML-1 (Section 4.6), Optional/recommended: Chapter 2 and Appendix B of PRML PPTX slides, PDF slides
4 Parameter Estimation and Prediction (contd) PPTX slides, PDF slides
5 Probabilistic Supervised Learning Bayesian inference tutorial (Section 1-3) PPTX slides, PDF slides
6 Probabilistic Supervised Learning (contd) Bayesian inference tutorial (Section 1-3) PPTX slides, PDF slides
7 Exponential Family Distributions PPTX slides, PDF slides
7 Generative Supervised Learning PPTX slides, PDF slides

Course Policies

Anti-cheating policy