CSC412S Spring 2003 - 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

Jan6/8 - Chapter 2.1 and Chapter 5.2
Jan13/15 - rest of Chapters 2 and 5, Chapter 13
Jan20 - Chapter 6.3,6.6,9.1,9.2 and Chapter 8
Jan22 - Chapter 7
Jan27 - no book chapter, see extra notes here
Jan29 - Chapter 9.4, Chapter 10 except EM algorithm
Feb3 - rest of Chapter 10, Chapter 11
Feb 5 - Chapter14
Feb24 - Bayesian model stuff and plates from chapter 5
Feb26 - Chapter 3
March3 -- Chapter 4 except factor graphs
March5,10 -- Chapter 12
March12,17,19 -- Chapter 17
March24 -- Chapter 18 HMM part
March26 -- Chapter 20
April 7 -- Chapter 4.2

Additional Material

A paper by Frank Kschischang and colleages on factor graphs.
Quaid Morris' QMR-DT page
Berkeley class notes on fully observed trees by Mike Jordan.
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.

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