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CSE 590ST - Statistical Methods in Computer Science - Spring 2004 |
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Instructor: Pedro Domingos
Office: Allen 648
Office hours: Wednesdays 3:00-3:50 and by appointment |
TA: Matt Richardson
Office: Allen 216
Office hours: Mondays 3:00-3:50 and by appointment |
Class meets:
Mondays and Wednesdays from 1:30 to 2:50 in MGH 231
Readings
Week 1: Chapter 2 of Koller & Friedman; review basic probability and statistics
Week 2: Chapters 3 and 4 of Koller & Friedman
Weeks 3 & 4: Chapters 7, 8, 9 and 10 of Koller & Friedman
Weeks 5 & 6: Chapter 15 of Koller & Friedman, Heckerman tutorial
Week 7: Section 20.3 of Russell & Norvig
Week 8: Chapter 5 of Koller and Friedman
Week 9: Chapter 15 of Russell & Norvig
Week 10: Chapters 16 and 17 of Russell & Norvig
Lecture Notes
Week 1: Introduction, background
Week 2: Bayesian networks (see also this)
Weeks 3 & 4: Inference in Bayesian networks (see also this)
Weeks 5 & 6: Learning Bayesian networks
Week 7: The EM algorithm
Week 8: Markov networks
Week 9: Temporal models
Week 10: Decision theory, Markov decision processes
Topics
The topics covered will have a non-null intersection with the following list:
- Bayesian networks
- Markov networks
- Markov chain Monte Carlo
- Belief propagation
- Mixture models
- Maximum likelihood and Bayesian estimation
- The EM algorithm
- Hidden Markov models
- Dynamic Bayesian networks
- Particle filters
- Relational models
- Decision theory
- Markov decision processes
- Information theory
Evaluation
Class evaluation will be by means of four homeworks, each worth 15% of the final grade, and a final, worth 40%. The homeworks include programming.
Homework assignments will be handed out on weeks 2, 4, 6 and 8, and will be due two weeks later.
Late policy: penalty of 2 points (out of 15) per day, up to a maximum of one week.
Homework 1
Homework 2
(And here are the networks referenced in the homework: ab.bif,
insurance.bif)
Homework 3
(And here are the files referenced in the homework:
alarm.bif, atrain.names, atrain.data, atest.names, atest.data)
Homework 4
Textbooks
- D. Koller & N. Friedman, Bayesian Networks and Beyond: Probabilistic Models for Learning and Reasoning, MIT Press.
This book has not been published yet; copies of chapters will be handed out in class.
- S. Russell & P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed.), Prentice Hall, 2003 (recommended).
- M. DeGroot & M. Schervish, Probability and Statistics (3rd ed.), Addison-Wesley, 2002 (recommended; 2nd ed. also OK).
Anonymous Feedback
Comments can be sent to the instructor or TAs using this
anonymous feedback form.
Course Mailing List
To subscribe to the course mailing list, visit the mailing list home page.
Alternatively, you can use the email interface to subscribe; send email to cse590st-request@cs with the word "help" in the subject to receive a list of email command options.
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Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX
[comments to
pedrod@cs.washington.edu] |
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