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: firstname.lastname@example.org
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.
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:
- Advanced Machine Learning, (Spring semester).
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
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
- 20% Mid-semester exam
- 35% End semester exam
- 15% Programming Homeworks
- 10% Class project
- 15% Two pre-announced quizzes
- 5% In-class online quizzes
(best n-2 of n quizzes used for grading. There will be no compensation for missed quizzes)
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