- Days: Monday/Wednesday
- Time: 11:00 p.m. - 12:20 p.m.
- Room: SE2 1304
- Instructor: Rina Dechter
Course Description
One of the main challenges in building intelligent systems
is the ability to reason under uncertainty, and one of the most successful
approaches for dealing with this challenge is based on the framework of
Bayesian networks, also called graphical models. Intelligent systems based on
Bayesian networks are being used in a variety of real-world applications
including diagnosis, sensor fusion, on-line help systems, credit assessment,
bioinformatics and data mining.
The objective of this class is to provide an in-depth exposition of
knowledge representation and reasoning under uncertainty using the framework
of Bayesian networks. Both theoretical underpinnings and practical
considerations will be covered, with a special emphasis on dependency and
independency models, on construction Bayesian graphical models and on exact
and approximate probabilistic reasoning algorithms. Additional topics
include: causal networks, learning Bayesian network parameters from data and
dynamic Bayesian networks.
- Familiarity with basic concepts of probability theory.
- Knowledge of basic computer science, algorithms and programming principles.
- Previous exposure to AI is desirable but not essential.
- Judea
Pearl, Probabilistic Reasoning in Intelligent Systems. Heckerman
& Breese, Causal
Independence for Probability Assessment and Inference Using Bayesian
Networks.
- Boutilier,
Friedman, Goldszmidt & Koller,
Context-Specific
Independence in Bayesian Networks.
- Dechter,
Bucket Elimination: A
Unifying Framework for Probabilistic Inference. 1998.
- Dechter,
"AAAI98
tutorial on reasoning."
- Heckerman, A Tutorial on Learning with Bayesian
Networks.
- Kjaerulff,
dHugin: A Computational System for Dynamic
Time-Sliced Bayesian Networks.
- Pearl, Causation,
Action and Counterfactuals.
- Dechter, Mini-buckets:
a general scheme for approximating inference.
Extended report.
- Darwiche,
Recursive
Conditioning: Any-space conditioning algorithm with treewidth-bounded
complexity.
- Darwiche, Any-space
probabilistic inference.
- Darwiche, On the role of
partial differentiation in probabilistic inference.
- Horvitz, Breese,
Heckerman, Hovel & Rommelse. The Lumiere Project: Bayesian User Modeling for
Inferring the Goals and Needs of Software Users.
- Binder, Murphy,
Russell. Space-efficient
inference in dynamic probabilistic networks.
- Russell, Binder, Koller, Kanazawa.
Local
learning in probabilistic networks with hidden variables.
- Dugad
& Desai. A
Tutorial on Hidden Markov Models.
- Friedman, Geiger, Goldszmidt. Bayesian
Network Classifiers.
- Dechter,
R., El Fattah, Y.,
Topological Parameters For Time-Space Tradeoff
- Gagliardi,
F., Generalizing
Variable Elimination In Bayesian Networks
- Rish,
I; Dechter, R, AAAI
2000 Tutorial
- Judea Pearl.
Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1990.
- K. B. Korb and A. E. Nicholson, Bayesian Artificial
Intelligence, Chapman and
Hall/CRC, 2004
- Finn V. Jensen. An
introduction to Bayesian networks. UCL Press, 1996.
- Russell
and Norvig, Artificial intelligence, a modern
approach (chapters, 13,14,15)
- J. Pearl, Causality,
Models, Reasoning and Inference, Cambridge University Press, 2000
- Castillo, E.;
Gutierrez, J.M.; Hadi, A.S., Expert Systems
and Probabilistic Network Models, Springer-Verlag
1997
- Robert G. Cowell, A. Philip Dawid,
Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert
Systems Springer-Verlag, 1999
There will be homework assignments and students will also
be engaged in projects.
Homeworks
and projects (50%), midterm (50%)
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