CSC412S Spring 2006 - 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

Jan9/11 - Chapter 2.1 and Chapter 5.2
Jan16/18 - rest of Chapters 2 and 5, Chapter 13, Chapter 16 ("4")
Jan23 - Chapter 6.3,6.6,9.1,9.2 and Chapter 8
Jan25 - Chapter 7
Jan30 - no book chapter, see extra notes here
Feb1 - Chapter 9.4, Chapter 10 except EM algorithm
Feb6 - rest of Chapter 10, Chapter 11
Feb 8 - Chapter 11
Feb 13 - Chapter14
Feb17 -- Chapter 20 (IPF Part)
Feb27 - Bayesian model stuff and plates from chapter 5
March1 - Chapter 3
March6 -- Chapter 4 except factor graphs
March8,13 -- Chapter 12
March20,22,27 -- Chapter 17
March29 -- Chapter 18 HMM part
March29 -- Chapter 4 (Factor Graphs)
April3 -- Chapter 19
April5 -- Chapter 20 (IPF and GIS)
April 10 --

NOT COVERED: Chapter 15 (Kalman Filtering), Chapter 21 (Sampling), Chapter 22 (Variational Inference), Chapter 29

Additional Material

Probability and Statistics Review.
Notes: [ps.gz] [pdf].
For a condensed overview of the course, see my tutorial notes from the 2005 Machine Learning Summer School in Canberra (MLSS05).
The slides are available from the mlss website here or locally here.
Darroch and Radcliff, Generalized Iterative Scaling for Log-Linear Models, [pdf]
Imre Csiszar, A Geometric Interpretation of Darroch and Radcliff's Generalized Iterative Scaling, [pdf]
Zoubin Ghahramani and Geoff Hinton, The EM algorithm for Mixtures of Factor Analyzers [ps.gz, pdf, 8 pages]
A paper by Frank Kschischang and colleages on factor graphs.
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 - Probabilistic Learning and Reasoning || www.cs.toronto.edu/~roweis/csc412/