Steam-powered Turing Machine University of Washington Computer Science & Engineering
 CSE 515 - Statistical Methods in Computer Science - Winter 2016
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Instructor: Pedro Domingos
Office: Allen 648
Office hours: Wednesdays 11:00am-11:50am and by appointment
TA: Yuyin Sun
Office: Allen 218
Office hours: Mondays 11:00am-11:50am and by appointment

Class meets:
Mondays and Wednesdays from 12:00 to 1:20 in EEB 037

Schedule

Week Dates Topics & Lecture Notes Readings
1 January 4 & 6 Introduction, basics of probability and statistical estimation Ch. 1, 2 & 17
2 January 11 & 13 Mixture models and the EM algorithm (EM notes) Ch. 19
3 January 20 (no class Jan. 18) Hidden Markov models and Kalman filters Ch. 6 (Sec. 6.1 & 6.2) & Ch. 15 (Sec. 15.4.1)
4 January 25 & 27 Bayesian networks and Markov networks Ch. 3 & 4
5 February 1 & 3 Variable elimination, junction trees and belief propagation Ch. 9 - 11
6 February 8 & 10 Sampling-based inference Ch. 12
7 February 17 (no class Feb. 15) Learning Bayesian networks Ch. 16 - 18
8 February 22 & 24 Learning Markov networks Ch. 20
9 February 29 & March 2 Dynamic Bayesian networks, particle filtering and relational models Ch. 15 (Sec. 15.1 - 15.3) & Ch. 6 (Sec. 6.3 & 6.4)
10 March 7 & 9 Decision theory and Markov decision processes Ch. 22 & 23

Textbook

D. Koller & N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press (required).

Assignments

There will be four homework assignments, each worth 25% of the final grade. The homeworks include programming. Homework assignments will be handed out on weeks 2, 4, 6, 8, and will be due two weeks later.

(Assignments will be posted here once they are ready.)

Course Administration and Policies


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