CS 725: Foundations of Machine Learning (Autumn 2026)


Lecture Schedule Slot 6, Wednesdays, Fridays: 9:30--10:55 am
Venue: LA002
Instructor: Sunita Sarawagi
Course email: cs725_iitb@googlegroups.com
TAs: Darshan Prabhu, Abhay, Aniket Waghela, Anirban Paul, Anshuman Dangwal, Deeksha Koul, Deeptanshu Malu, Deevyanshu Malu, Shubham Dagar.
Instructor's office hours check here
Course announcement, internal notes : moodle.iitb.ac.in
Syllabus and week-wise calendar: Click here

Prerequisites

An upper-level undergraduate course(s) in algorithms and data structures, a basic course on probability and statistics, basic understanding of linear algebra and multivariate calculus. The first 2--3 quizzes will be designed to test your background on these topics. If you feel you need to revise these topics, the first part of the Kevin Murphy book below is a compact resource. If you get poor grades in these quizzes, you may want to drop the course. The course will heavily require knowledge of these topics.

This is a first course on machine learning and no prior knowledge of machine learning is assumed.
This is an introductory ML course. If you have taken another introductory ML course in IITB in your respective departments, you are not allowed to take this course. You will be assigned an FR grade for violating this rule. We do not want to burden TAs with evaluating such students.

Eligibility

The course is open only to Masters and PhD students of IITB who meet the necessary pre-requisites. UGs are not permitted. Please do not write for special permission.

Credit/Audit Requirements

Approximate credit structure Audit Students: Audit students will get a pass if they attend at least 90% of the lectures, and get at least 70% marks in the in-class online quizzes. Due to our limited TA support, we will *not* grade exams and projects of audit students.

Reading Material

Weekwise course calendar and classnotes (if any) will be made available on Moodle. Other reference books appear below.

Reference books

[ KM22]
``Probabilistic Machine Learning'' by Kevin Murphy. MIT Press, Mar 2022, Available online.
[ SS17]
Understanding Machine Learning. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press. 2017. Available online.
[ Bis07]
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. website
[ HTF]
Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
[Mit97]
[DDL2019]
Dive into Deep Learning Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola, 2019

Supplementary books

[ PRS ]
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
[ BV ]
Boyd and Vandenberghe Convex optimization Book available online:
GBC16
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
[ GS ]
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.

Other useful resources