Probabilistic Machine Learning
CS772A
2022-23 (even semester)

Class Timings: Mon/Thur 18:00 - 19:30
Class Venue: KD-101
Instructor: Piyush Rai (Office RM-502, email: piyush AT cse DOT iitk DOT ac DOT in)
Office Hours: Wed 16:00-17:00 or by appointment (RM-502)
Q/A Forum: Piazza
TAs and email: Avideep Mukherjee (avideep AT cse), Soumya Banerjee (soumyab AT cse), Malay Pandey (malay AT cse) TA Office Hours: TBD

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

(Tentative) There will be 4 homeworks (40%) which will consist of a mix of written and programming questions, 2 quizzes (5% each), 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:

Kevin Murphy, Probabilistic Machine Learning: An Introduction (PML-1), MIT Press, 2022 (free PDF draft available online)
Kevin Murphy, Probabilistic Machine Learning: Advanced Topics (PML-2), MIT Press, 2022 (free PDF draft available online)
Christopher Bishop, Pattern Recognition and Machine Learning (PRML), Springer, 2007 (free PDF draft available online)

Schedule

Lec. No.	Date	Topics	Readings/References/Comments	Slides/Notes
1	Jan 5	Course Logistics, Intro to Probabilistic Machine Learning	A review article on PML and AI, Probability and statistics refresher slides, PML-2 Sec 3 - 3.1.5.2

Useful Links and Softwares

Course Policies

Anti-cheating policy

Probabilistic Machine Learning CS772A 2022-23 (even semester)