CS 725: Foundations of Machine Learning (Autumn 2019)

Lecture Schedule Slot 5, Wednesdays, Fridays: 9:30--11am.
The first class will be held on Friday 2nd August 2019.
Venue LH 301
Instructor: Sunita Sarawagi
Course email: cs725_iitb@googlegroups.com
TAs: Abhijeet Awasthi, Saurabh Malusare, Punjabi Mayur Kishor, Kamlesh Marathe, Achari Rakesh Prasanth , Rishab
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. This is a first course on machine learning and no prior knowledge of machine learning is assumed. Homework assignments will require programming in Python. If you do not already know Python, you should be able to pick up easily if you know C++ or Java or Matlab. This tutorial is a good starting point.


This course or CS419m or CS 337 is a prerequisite for the following courses:


The course is open only to CS MTechs and PhD students. UGs are not permitted and can instead take either 419m or CS 337. Other students should approach for permission only if they meet the necessary pre-requisites.

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. 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 own.

Reading List

Primary books

[ 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
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:
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