F E E D B A C K

CSE561 - Probabilitistic Graphical Models

IIIT-Delhi

Winter 2020

4 credits

Instructor

Anubha Gupta

Teaching Assistants

Ashutosh Vaish
Mohit Rathore

Info - Probabilitistic Graphical Models, IIIT-Delhi

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 - 10
2. Assignment -15
3. Mid-sem - 20
4. End-sem - 10
5. Project + Paper 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