
CSC2515 Fall 2002  Weekly and Other Readings
Textbook
There is no required textbook for the class.
I will be handing out some class notes as we go along.
Some classic papers will be assigned as readings.
We will also be covering material similar to a variety of chapters from
a few books which I will point out in class.
Weekly Readings
 September 11
L.G. Valiant, A Theory of the Learnable [pdf, 9pages]
 Semptember 18 (choose one)
R.A. Fisher, The Use of Multiple Measurements in Taxonomic
Problems [pdf, 10pages]
or
J.R. Quinlan, Induction of Decision Trees
[in class,14pages]
 September 25
Michael Jordan,
Why the logistic function?
[pdf ,
ps.gz, 13pages]
 October 2
Robert Tibshirani
Regression shrinkage and selection via the lasso
[pdf ,
ps.gz, 28pages]
 October 9
Rumelhart, Hinton and Williams,
Learning representation by backpropagating errors,
(Nature, 1986) [in class, 4 pages].
 October 16
Michael I. Jordan and Robert A. Jacobs (1994),
Hierarchical Mixtures of Experts and the EM Algorithm
[pdf ,
ps.gz, 36pages]
Note: only read pages 110 this week.
 October 23
rest of the Mixtures Experts article
 October 30
Geoff Hinton and Radford Neal,
A View of the EM Algorithm, Learning in Graphical Models (1998),
[pdf ,
ps.gz, 14pages]
 November 6 and November 13
Sam Roweis and Zoubin Ghahramani,
A Unifying Review of Linear Gaussian Models,
Neural Compuation (1999),
[pdf
41pages]
Skip sections 5.4, 6.2, 8, 9, A.3
 November 13
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
[pdf7pages]
 November 20
Golub, Heath, Wahaba,
Generalized Cross Validation, Technometrics 1979.
[in class, 9 pages]
 November 27
Rob Shapire, The Strength of Weak Learnability., Machine
Learning 1990. [in class, 31 pages]
 December 4
Corinna Cortes and Vladimir Vapnik, Support Vector Networks,
Machine Learning 20(3): 273297 (1995)
[ps.gz31pages]
Additional Material
 Probability and Statistics Review
[ps.gz].
 A tutorial
by Andrew Moore at CMU on Bayesian methods.
 Another
tutorial
by Andrew Moore on probabilities
for machine learning and data mining.
 A short MATLAB tutorial.
 Pedro Domingos, Michael Pazzani,
On the Optimality of the Simple Bayesian Classifier
[pdf, 28pages]
 David Mackay, Maximum Likelihood and Covariant Algorithms for
ICA [ps.gz, 15 pages]
 Zoubin Ghahramani and Geoff Hinton,
The EM algorithm for Mixtures of Factor Analyzers
[ps.gz, 8 pages]
 Leo Brieman, Bagging Predictors
[pdf, 20 pages]
 David Wolpert, Stacked Generalization
[ps.gz, 57 pages]
 Rob Shapire, Boosting Overview
[ps.gz, 23 pages]
Draft Book Chapters
Linear Algebra, v1.2 [ps.gz].
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CSC2515  Machine Learning  www.cs.toronto.edu/~roweis/csc2515/
