Introduction to Probabilistic Graphical Models

Fall, 2011
College of Computing
Georgia Institute of Technology

This course will cover basic and advanced techniques in Probabilistic Graphical Models. The goal of this class is to gain theoretical and practical understanding of these models, which include many standard probability models such as mixture models, Hidden Markov Models, Markov Random Fields, and so forth. We will also describe the application of PGMs to real-world problems, drawn from computer vision and other fields.

8803PGM Introduction to Probabilistic Graphical Models
MWF 3:00-4:00
CCB 102


Jim Rehg
Office Hours: MF 4-5

Teaching Assistant

David Tsai
Office Hours: TBD


Familiarity with basic probability, graph theory, and linear algebra. This is an introductory course and the lectures will be self-contained.


There are two required texts for this course:

Assignments and Grading

Each Mon you will be assigned a short problem set which will be due by midnight the following Sunday evening. You will submit your solutions using T-Square. These problems sets will consist of exercises from the Koller and Friedman book, along with other sources. In addition, there will be three assigned mini-projects. There will also be an open book, in-class final exam.

All work must be submitted by the deadline. Late work will be assessed a penalty of 10 points deducted per day.


Weekly Assignments

Mini Projects

Each student will do three mini-projects on the following topics:

For each project, we will provide you with some scaffolding code and you will propose a topic and work on it. For example, within LDA you might propose to model a particular text corpus of interest and then use the resulting LDA model to accomplish some task. Or you might propose to compare two approximate learning methods such as variational EM and Gibbs sampling, to see how they perform. We will provide the basic code base, you get to decide how to use it and what to study. We will provide Matlab code to support each of these projects. In addition, we can point you to codes in other languages if you are not interested in using Matlab, or want to work on larger model sizes where Matlab may be too slow.


Remaining Topics (to be covered)