CSE561 - Probabilitistic Graphical Models
IIIT-Delhi
Winter 2020
4 credits
Instructor
Anubha Gupta
Teaching Assistants
Ashutosh Vaish
Mohit Rathore
Overview
This course will introduce the basic concepts of probabilistic graphical models. Graphical Models are a unified framework that allow to express and manipulate complex probability distributions in a compact and efficient way. They allow to one to reach mathematically sound conclusions in presence of limited and noisy observations. Many machine learning applications are tackled by the use of these models.
Description
Course Objectives:
CO1. Students are able to construct
Bayesian and Markov network
representation for a given problem.
CO2. Students will know and able to apply techniques to do exact
and approximate inference in the probabilistic graphical
models.
CO3. Students are able to understand
how to learn parameters and
structure for graphical models.
Evaluation
1. Quiz
2. Assignment
3. Mid-sem
4. End-sem
5. Project + Presentation - 30
Class Timings
Tuesdays : 12:00-1:30 pm
Thursdays : 10:30-12:00 pm
Office Hours
Office hour of Instructor : Friday (4:30-5:30 pm)
Textbooks
Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman, MIT Press, 2009