Probabilistic Graphical Models

10-708, Fall 2006

School of Computer Science, Carnegie-Mellon University

Lectures: Wednesdays and Fridays, noon-1:30pm in Wean 5409
Recitations: Thursdays 5-6:30pm in Wean 4615A
Special Recitations: At some points in the semester, we will hold special, optional recitations on Mondays 5:30-7pm in Wean 4615A, we will announce these events in advance

Announcements

Final exam released
Starting date: September 13th, 2006
Mailing Lists:

To contact the instructors: 10708-instr@cs.cmu.edu
Class announcements list: 10708-announce@cs.cmu.edu.

Please, subscribe here

Textbook:

Primary: Daphne Koller and Nir Friedman, Bayesian Networks and Beyond, in preparation. These chapters are part of the course reader. You can purchase one from Monica Hopes.
Secondary: M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of selected chapters will be made available.

Your Instructors

Instructor:

Carlos Guestrin, Wean Hall 5313, Office hours: Tuesdays 2:30-3:30pm (Starting Sept. 15th)

TAs:

Khalid El-Arini, Wean Hall 7110, Office hours: Mondays 1:30pm-3:30pm (starting September 25)
Ajit Singh, Wean Hall 5130, Office hours: Wednesdays 2pm-4pm (starting September 27)

Class Assistant:

Monica Hopes, Wean Hall 4619, x8-5527

Course Description

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The general graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

The class will cover three aspects: The core representation, including Bayesian and Markov networks, dynamic Bayesian networks, and relational models; probabilistic inference algorithms, both exact and approximate; and, learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.