CS 726: Advanced Machine Learning (Spring 2025)
Lecture Schedule Slot 6, Wed-Fri 11--12:30pm
Venue
Instructor: Sunita Sarawagi
TAs: Prateek Garg, Atharva Tambat, Prayas Agrawal, Mridul Agarwal, Srihari, Nikita Verma, M Swetha, Afrin Dange, Annie Dsouza, Ajay Pathak
Email to reach all TAs and Instructor CS726@googlegroups.com.
For questions of general interest to the class, use moodle.
Instructor's office hours check here
Syllabus and week-wise calendar: 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, deep generative models like diffusion models, variational auto-encoders, GANs, Gaussian processes, 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 217 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. Also, there will be programming assignments that will benefit from fluency in Python.
Credit/Audit Requirements
Approximate credit structure
- 15% In-class Quizzes
- 10% Graded homeworks
- 25% Mid-semester exam
- 35% End semester exam
- 15% Project
Audit students have to score more than 30% over all, spanning any of
assignments, quizzes and mid/end sem exam. Also, audit students need to appear in at least 80% of the class quizzes.
Reading List
Primary text books for part of the course
Probabilistic Graphical Models: Principles and Techniques,
by Daphne Koller and Nir Friedman, MIT Press, 2009.
The course calendar will provide links
to other relevant papers and book chapters for specific topics.