CSE 590ST - Statistical Methods in Computer Science - Spring 2004
| 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
Mondays and Wednesdays from 1:30 to 2:50 in MGH 231
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
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
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
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.
(And here are the networks referenced in the homework: ab.bif,
(And here are the files referenced in the homework:
alarm.bif, atrain.names, atrain.data, atest.names, atest.data)
- 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).
Comments can be sent to the instructor or TAs using this
anonymous feedback form.
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Department of Computer Science & Engineering
University of Washington
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX