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Probabilistic Graphical Models10-708,
Fall 2005
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Announcements |
The final exam is out. The exam must be done individually. The exam is due Dec 15th by 2pm electronically to 10708-instr@cs.cmu.edu, or paper version to Monica Hopes, or by fax to 412-268-3431.
The poster session for the class projects will be held on Friday Dec. 2nd, 1:30-4:30pm, in NSH 3305.
- The project details are out.
- Starting date: September 12, 2005
- Class lectures: Mondays & Wednesdays from 3:30-4:50pm
- Location: NSH 3002
- Recitation: Thursdays, 5 - 6pm, Wean Hall 4615A.
- Mailing Lists:
- To contact the instructors: 10708-instr@cs.cmu.edu
- Class announcements list: 10708-announce@cs.cmu.edu.
- Textbook:
- M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of chapters will be made available.
- Daphne Koller and Nir Friedman, Bayesian Networks and Beyond, in
preparation. Copies of chapters will be made available.
People |
Instructors:
- Eric Xing, Wean Hall 4127, Office hours: Wednesdays 5:30-6:30pm
- Carlos Guestrin, Wean
Hall 4610, Office hours: Tuesdays 3:30-4:30pm
TA:
- Pradeep Ravikumar
- Monica
Hopes
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.
Students are required
to have successfully
completed 10701/15781, or an equivalent class.
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