CS583: Probabilistic Graphical Models - Spring 2012

Course Description

This course will cover probabilistic graphical models -- powerful and interpretable models for reasoning under uncertainty. The generic families of models such as directed, undirected, and factor graphs as well as specific representations such as hidden Markov models and conditional random fields will be discussed. The discussions will include both the theoretical aspects of representation, learning, and inference, and their applications in many interesting fields such as computer vision, natural language processing, computational biology, and medical diagnosis.

Course Topics

The following is a tentative and partial list of topics that will be covered in the class:

Course Information

Time and Location: Tue - Thu 3:15 - 4:30pm in Stuart Building 239
Mustafa Bilgic
Office: Stuart 228C
Email: mbilgic AT iit.edu
Office Hours: Tue - Thu 11am - 12pm (Other times by appointment)

Required background

Knowledge of probability and statistics is required. CS480 and CS584 are recommended but not required.

Course Format and Grading

In my couse, the slides often serve only as a guide; I use the white board heavily. 

The evaluation will consit of written assignments, a project, a midterm, and a final. The point breakdown is:

Late submission policy: You have seven free late days without penalty. You can use those days in any way you like for the written assignments and/or the project; you can use all of them for a single assignment or use them sparingly. However, once you run out of free days, late submissions will not be accepted. Absolutely positively no exceptions. So, use them wisely.
Collaboration policy: You can discuss the written assignments with your friends; however, everyone has to write their own solutions in their own words. You must include in your submissions the name of the people you collaborated with.
Code of academic honesty: Please read the procedures on academic honesty here. If you violate the academic honesty (such as unauthorized collaboration, cheating, etc.), then depending on the severity of the violation, it can result in i) getting zero points on the respective assignment, ii) expulsion from the course, iii) suspension of your enrollment at the university, iv) expulsion from the university.

Course Material

There is a required text book for this course:

Probabilistic Graphical Models, by Daphne Koller and Nir Friedman

There will be additional reading materials (mostly available on the web).

Tentative Schedule

Date Topic Reading
Jan 10 Syllabus & Introduction Ch. 1
Jan 12 Foundations Ch. 2
Jan 17 Bayesian networks Ch. 3
Jan 19
Jan 24 Markov networks Ch. 4
Jan 26
Jan 31 Local probabilistic models Ch. 5
Feb 02
Feb 07 Template-based representations Ch. 6
Feb 09 Variable Elimination Ch. 9
Feb 14
Feb 16 Clique Trees Ch. 10
Feb 21
Feb 23 Approximate Inference Ch. 12
Mar 06 Approximate Inference Ch. 12
Mar 08 MAP Inference Ch. 13
Mar 13 Inference in temporal models Ch. 15
Mar 15 Learning – overview Ch. 16
Mar 22
Mar 27 Parameter estimation Ch. 17
Mar 29
Apr 03 Structure learning in Bayesian networks Ch. 18
Apr 05
Apr 10 Learning undirected models Ch. 20
Apr 12 Collective classification
Apr 17
Apr 19 Hidden Markov models
Apr 24 Conditional random fields