CS 726: Advanced Machine Learning (Spring 2022)


Lecture Schedule Slot 9, Mon-Thurs 3:30pm to 5:00pm.
Venue msteams
Instructor: Sunita Sarawagi
TAs: Prathamesh Deshpande, N Lokesh, Keshav Agarwal, Tathagat Verma
Email to reach all TAs and Instructor CS726@googlegroups.com.
For questions of general interest to the class, use moodle.
Instructor's office hours 7pm to 7:30pm on Mondays and Thursdays. Send me an email if you plan to avail of the office hours.
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, MCMC sampling methods like Gibbs and Langevin, generative models like variational auto-encoders, GANs, Deep Gaussian processes, neural architectures for structured predictions, neural density estimation methods, causality, and other recent topics in machine learning. When appropriate the techniques will be linked to applications in NLP, speed recognition, vision, graphics, and sciences.

Eligibility

The course is open to PhD, Masters, DD and BTech students provided they have taken an introductory course in IITB in machine learning (listed below) and obtained at least a BC grade in it.

Prerequisites

A formal introductory ML course like CS 725 or CS 337 or CS 419 or DS 303 is required. Online ML courses do not qualify as pre-requisites. 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. Further, we will assume that students are familiar with CNNs, RNNs, and sequence to sequence learning with attention.

Credit/Audit Requirements

Approximate credit structure

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