CSC412S/2506S Spring 2004 - Textbook and Other Readings

Textbook

The textbook for the class is Michael Jordan, An Introduction to Probabilistic Graphical Models
This textbook is not yet published, but drafts are available online.

Click here to access the book.

The user name and password will be provided in class.

Readings (tentative list, watch for updates)

Jan5/7 - This article, plus Sections 2.0-2.1, and 5.0-5.2 of the book.
Jan 9 -- Probability and statistics review notes and Chapter 13.
Jan12/14 - rest of Chapters 2 and 5, Chapter 8
Jan19 - Chapter 9.0-9.2, Chapter 6 except 6.2,6.4, review Chapter 8
Jan21 - Chapter 7
Jan26 - no book chapter, see extra notes here
Jan28 - rest of Chapter 9, Chapter 10 except EM algorithm
Feb2/4 - rest of Chapter 10, Chapter 11
Feb 9 - Chapter14
Feb 11 - Chapter 9.3-9.5
Feb 11 -- Chapter 4.2 (Factor Graphs)
Feb23 - Bayesian model stuff and plates (review parts of Chapter 5)
Feb25 - Chapter 3
March1 -- Chapter 4 except message passing on factor graphs
March 3 -- first part of Chapter 17
March8,10 -- Chapter 12
March15,17 -- rest of Chapter 17
March19 -- Chapter 18 (HMM part)
March24 -- Chapter 19
March29 -- Chapter 20

Additional Material

Some notes from Ron Shamir at Tel Aviv University on the equations for Profile HMMs.
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988. [pdf7pages]
Zoubin Ghahramani and Geoff Hinton, The EM algorithm for Mixtures of Factor Analyzers [ps.gz, 8 pages]
A note by Tom Minka on Graphical Models (which he calls Independence Diagrams).
An article in AI magazine by Eugene Chaniak entitled Bayesian Networks without Tears.
A tutorial on learning with Bayesian networks by David Heckerman.
A short MATLAB tutorial.
A paper by Frank Kschischang and colleages on factor graphs.
Berkeley class notes on fully observed trees by Mike Jordan.
Quaid Morris' QMR-DT page

CSC412/2506 - Uncertainty and Learning in Artificial Intelligence || www.cs.toronto.edu/~roweis/csc412/