Steam-powered Turing Machine University of Washington Computer Science & Engineering
 CSE 515 - Statistical Methods in Computer Science - Winter 2017
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Instructor: Pedro Domingos
Office: Allen 648
Office hours: Thursdays 10:00am-10:50am and by appointment
TA: Alon Milchgrub
Office: Allen 220
Office hours: Tuesdays 10:00am-10:50am and by appointment

Class meets:
Tuesdays and Thursdays from 11:00 to 12:20 in EEB 003

Schedule

Week Dates Topics & Lecture Notes Readings
1 January 3 & 5 Introduction, basics of probability and statistical estimation Ch. 1, 2 & 17
2 January 10 & 12 Mixture models and the EM algorithm (EM notes) Ch. 19
3 January 17 & 19 Hidden Markov models and Kalman filters Ch. 6 (Sec. 6.1 & 6.2) & Ch. 15 (Sec. 15.4.1)
4 January 24 & 26 Bayesian networks and Markov networks Ch. 3 & 4
5 January 31 & February 2 Variable elimination, junction trees and belief propagation Ch. 9 - 11
6 February 7 & 9 Sampling-based inference Ch. 12
7 February 14 & 16 Learning Bayesian networks Ch. 16 - 18
8 February 21 & 23 Learning Markov networks Ch. 20
9 February 28 & 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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