CS 725: Foundations of Machine Learning (Autumn 2023)
Lecture Schedule Slot 5, Wednesdays, Fridays: 9:30--10:55 am
Venue: LA 002
Instructor: Sunita Sarawagi
Course email: email@example.com
TAs: Lokesh N, Vishak Prasad C, Ashutosh Sathe, Krishnakant Bhatt, Meet Doshi
Instructor's office hours check here
Course announcement, internal notes : moodle.iitb.ac.in
Syllabus and week-wise calendar: Click here
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. If you
feel you need to revise these topics, you can find some resources below. Chapters 2,3,and 4 of the Deep learning book is a compact resource. Homework assignments will require
programming in Python.
This is a first course on machine learning and no prior knowledge of
machine learning is assumed.
course is open only to CS and CMInDS Masters and PhD students. PG
students of other departments can write for permission if they meet
the necessary pre-requisites. UGs are not permitted. Please do
not write for special permission.
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. Audit students are also allowed to
participate in the first programming assignment. If they do, then
their 70% cutoff will be calculated based on the combination of SAFE
quiz and marks in that assignment. Due to our limited TA support, we
will *not* grade all assignments or exams of audit students. However,
audit students are welcome to do the assignments and projects on their
- 25% Mid-semester exam
- 35% End semester exam
- 12% Three programming homeworks (Before midsems)
- 15% Class project (After midsems)
- 10% In-class quizzes
(best n-2 of n quizzes used for grading. There will be no compensation for missed quizzes)
- Scribe class notes (3%)
Weekwise course calendar and classnotes (if any) will be made available on Moodle. Other reference books appear below.
``Probabilistic Machine Learning'' by Kevin Murphy. MIT Press, Mar 2022, Available online.
Understanding Machine Learning. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press. 2017. Available online.
Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. website
Hastie, Tibshirani, Friedman
The elements of Statistical Learning
Dive into Deep Learning
Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola, 2019
Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
Boyd and Vandenberghe
Convex optimization Book available online:
by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.
Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
Other useful resources