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 point
breakdown is:
There are three types
of projects:
No matter which project type you choose, all projects require:
data, coding, experiments, analysis, and a report.
There is a recommended
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 13 | Syllabus & Introduction | Ch. 1 |
Jan 15 | Foundations | Ch. 2 |
Jan 20 |
Martin Luther King Day - No class |
|
Jan 22 |
Bayesian networks | Ch. 3 |
Jan 27 |
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Jan 29 |
Markov networks | Ch. 4 |
Feb 03 |
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Feb 05 |
Local probabilistic models | Ch. 5 |
Feb 10 |
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Feb 12 |
Template-based representations | Ch. 6 |
Feb 17 |
Variable Elimination | Ch. 9 |
Feb 19 |
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Feb 24 |
Clique Trees | Ch. 10 |
Feb 26 | ||
Mar 3 |
Approximate Inference | Ch. 12 |
Mar 5 |
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Mar 10 |
MIDTERM EXAM | |
Mar 12 |
MAP Inference | Ch. 13 |
Mar 17 |
SPRING BREAK | |
Mar 19 |
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Mar 24 | Learning – overview | Ch. 16 |
Mar 26 | Parameter estimation | Ch. 17 |
Mar 31 |
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Apr 02 | Structure learning in Bayesian networks | Ch. 18 |
Apr 07 | ||
Apr 09 |
Learning undirected models | Ch. 20 |
Apr 14 | ||
Apr 16 | Collective classification | |
Apr 21 | ||
Apr 23 | Hidden Markov models | |
Apr 28 | Conditional random fields | |
Apr 30 |
REVIEW |