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.
Time and Location: Mon - Wed 1:50 - 3:05pm in Stuart
Building 220
Professor: Mustafa
Bilgic
Office: Stuart 228C
Email: mbilgic AT iit.edu
Office Hours: Wed 11am - 12pm (Other times by appointment)
In my course, the slides often serve only as a guide; I use the white board heavily.
The evaluation will
consist a midterm, a
semester-long project, and a final. The project will
be on a topic of your choice (ideally a research project) and in
any programming language you like. The point breakdown is:
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).
Date | Topic | Reading |
---|---|---|
Jan 14 | Syllabus & Introduction | Ch. 1 |
Jan 16 | Foundations | Ch. 2 |
Jan 21 |
Martin Luther King Day - No class |
|
Jan 23 |
Bayesian networks | Ch. 3 |
Jan 28 |
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Jan 30 |
Markov networks | Ch. 4 |
Feb 04 |
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Feb 06 |
Local probabilistic models | Ch. 5 |
Feb 11 |
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Feb 13 |
Template-based representations | Ch. 6 |
Feb 18 |
Variable Elimination | Ch. 9 |
Feb 20 |
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Feb 25 |
Clique Trees | Ch. 10 |
Feb 27 | ||
Mar 4 |
Approximate Inference | Ch. 12 |
Mar 6 |
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Mar 11 |
MIDTERM EXAM | |
Mar 13 |
MAP Inference | Ch. 13 |
Mar 18 |
SPRING BREAK | |
Mar 20 | ||
Mar 25 | Learning – overview | Ch. 16 |
Mar 27 | Parameter estimation | Ch. 17 |
Apr 01 |
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Apr 03 | Structure learning in Bayesian networks | Ch. 18 |
Apr 08 | ||
Apr 10 | Learning undirected models | Ch. 20 |
Apr 15 | ||
Apr 17 | Collective classification | |
Apr 22 | ||
Apr 24 | Hidden Markov models | |
Apr 29 | Conditional random fields | |
May 01 |
REVIEW |