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.)

• Assignment 1, due at noon on January 27th.
  Data: Seeds.train, Seeds.test, Seeds-true.train, Seeds-true.test.
  See also: Seeds dataset at the UCI Machine learning repository
• Assignment 2, due at noon on February 10th.
  Data and code: Package.zip
• Assignment 3, due at noon on February 24th.
  Supplementary files: hw3-dist.zip (including skeleton code, .BIF files, the VFML library, and scripts).
  See Kschischang et al. for a detailed explanation of BP.
• Assignment 4, due at noon on March 9th.

Course Administration and Policies

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• Assignments will be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
• Dropbox to turn in homework.
• Gradebook
• Assignments may be handed in up to a week late, at a penalty of 10% of the maximum grade per day.
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• Comments can be sent to the instructor or TA using this anonymous feedback form