CS 726: Advanced Machine Learning (Spring 2018)

Lecture Schedule Slot 1, Monday: 8:30--9:25, Tuesday: 9:30--10:25, Thursday: 10:35--11:30
Venue CC 103
Instructor: Sunita Sarawagi
TA: Ujjwal Jain, Sukalyan Bhakat, Sandeep Subramanian, Vertika Srivastava,
Instructor's office hours check here
Syllabus and week-wise calendar: Click here
Homeworks Click here

Course description

This course will concentrate on modeling, generation, and prediction of multiple inter-dependent variables. The topics covered will span probabilistics graphical models (directed and undirected), inference methods like junction trees, belief propagation, and other approximate methods, sampling methods like MCMC, variational auto-encoders, GANs, neural architectures for sequence and graph-structured predictions. When appropriate the techniques will be linked to applications in translation, conversation modeling, speed recognition, graphics, and science.


CS 725 or an equivalent introductory course on machine learning. The course assumes basic knowledge of probability, statistics, and linear algebra. Chapters 2 and 3 of the Deep-learning book are a good place to refresh the necessary required background. Also, the course assumes basic background in machine learning, for example as covered in Chapter 5 of the Deep-learning book and deep learning, for example, as covered in Chapter 6 of the same book. A quiz will be administered within the course drop deadline to help you assess if you have the requisite background for the course.


The course is open to CS MTechs, PhD, DD and BTech students. Students of other departments should approach for permission only if they meet the necessary pre-requisites. Third year BTech students need to take prior permission from the instructor for enrolling in the course.

Credit/Audit Requirements

Approximate credit structure

Audit students have to score more than 30% over all, including assignments, quizzes and mid/end sem exam.

Reading List

Primary text books

Probabilistic Graphical Models: Principles and Techniques, by Daphne Koller and Nir Friedman, MIT Press, 2009.

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016.

The course calendar will provide links to other relevant papers and book chapters for specific topics.
Other relevant text books:

Online courses on deep learning