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Probabilistic Graphical Models 10-708, Fall 2007 Eric
Xing
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Announcements |
- Final is out.
- Starting date: September 10, 2007
- Class lectures: Mondays & Wednesdays from
10:30-11:50am
- Location: Wean 4623
- Recitations : Fridays 5-6 pm, Weh 4625 (Schedule)
- Mailing Lists:
- To contact the instructors:
10708-07-instr@cs.cmu.edu
- Class announcements list:
10708-07-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: TBA
TA:
- Ramesh Nallapati, Wean
Hall, Office hours: TBA
- Hetunandan Kamichetty, Doherty 4302C, Office hours: Wednesdays, 5:00-6:00 pm
Class Assistant:
- Monica
Hopes, Wean Hall 4616, 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 probabilistic 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,
and dynamic Bayesian networks; 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.
It is expected that after taking this class, the students should have
obtain sufficient working knowledge of multi-variate probablistic
modeling and inference for practical applications, should be able
to fomulate and solve a wide range of problems in their own domain
using GM, and can advance into more specialized technical literature by
themselves.
Students are required to have successfully completed 10701/15781, or an equivalent class.
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