Prerequisites: Basic Probability, Statistics, Linear Algebra

Course Description:

This course aims to provide an introduction to the area of probabilistic models
based on graphs, and meanwhile to offer an overall view on examining the recent
trends of using graphical models in various application domains such as machine
learning, sensor network, bioinformatics, signal/multimedia processing, and
computer vision, etc. Through the study of key models, e.g., Bayesian networks,
state space models, Markov random fields, and conditional random fields, we will
investigate and test several popular algorithms related to exact or approximate
inference, including elimination algorithm, junction tree, sum-product and max-
product algorithms, loopy belief propagation, and variational methods. Students
taking the course are required to submit a final term project that emphasizes an
appropriate use of graphical model techniques on their choice of application.

Course Contents:

1. Basic probability theory and graph theory

2. Representations of graphical models

3. Learning from data

4. Methods for exact inference

5. Methods for approximate inference

6. Applications

Daphne Koller and Nir Friedman
"Probabilistic Graphical Models: Principles and Techniques"
The MIT Press, 1 edition, 2009