CSC412S Spring 2006 - Textbook and Other Readings
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.
NOT COVERED: Chapter 15 (Kalman Filtering), Chapter 21 (Sampling),
Chapter 22 (Variational Inference), Chapter 29
- 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
- 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 --
- Probability and Statistics Review.
- 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
- Darroch and Radcliff, Generalized Iterative Scaling for
Log-Linear Models, [pdf]
- Imre Csiszar, A Geometric Interpretation of Darroch and
Radcliff's Generalized Iterative Scaling,
- Zoubin Ghahramani and Geoff Hinton,
The EM algorithm for Mixtures of Factor Analyzers
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.
Course Information |
Lecture Schedule/Notes |
CSC412 - Probabilistic Learning and Reasoning || www.cs.toronto.edu/~roweis/csc412/