Topics in Probabilistic Modeling and Inference
2017-18 (even semester)

Instructor: Piyush Rai: (office: RM-502, email: piyush AT cse DOT iitk DOT ac DOT in)
Office Hours: Wednesday 11:00am-12:00pm (or by appointment)
Q/A Forum: Piazza
Class Venue: KD-101
Class Timings: T/Th 5:10-6:30pm
TAs: Shivam Bansal, Smrithi Prabhu, Vinay Verma
TA Office Hours: TBD

Background and Course Description

Probabilistic models for data are ubiquitous in many areas of science and engineering, and specific domains such as visual and language understanding, finance, healthcare, biology, climate informatics, etc. This course will be an advanced introduction to probabilistic models of data (often through case studies from these domains) and a deep-dive into advanced inference and optimization methods used to learn such probabilistic models. This is an advanced course and ideally suited for student who are doing research in this area or are interested in doing research in this area.


Instructor's consent. The course expects students to have a strong prior background in machine learning (ideally through formal coursework), and ideally also some prior exposure/appreciation to basic principles of probabilistic modeling. The students are expected to have strong foundations in probability and statistics, linear algebra, and optimization, and must also be proficient in programming in MATLAB, Python, or R.

Schedule (Tentative)

Date Topics Readings/References Slides/Notes
Jan 4 Logistics and Introduction to the course Nature article on probabilistic modeling, Probability Refresher slides slides, (print version)
Jan 9 Basics of Probabilistic Modeling and Inference, Single-Parameter Models Parameter Estimation (only up to section 3), BDA Section 1.1-1.3, 1.8, BDA 2.1-2.6 slides, (print version)
Jan 11 Single-Parameter Models (Contd.), Intro to Bayesian Linear Regression BDA 2.1-2.6, MLAPP 7.1-7.3, 7.6 (7.6.1-7.6.2) slides, (print version)
Jan 11 Bayesian Linear Regression (Contd) PRML 3.3, MLAPP 7.1-7.3, 7.6 (7.6.1-7.6.2) slides, (print version)

A tentative set of topics to be covered includes:

Reference materials

We will primarily use lecture notes/slides from this class. In addition, we will refer to monographs and research papers for some of the topics. Some recommended, although not required, books are: