
CSC2515 Fall 2003  Weekly and Other Readings
Textbook
The textbook for the class is
Hastie, Tibshirani, Friedman
Elements of Statistical Learning.
I will also be handing out class notes as we go along.
Some classic papers will be assigned as weekly readings.
We will also be covering material similar to a variety of chapters from
a few other books which I will point out in class.
Weekly Readings
 September 10
L.G. Valiant, A Theory of the Learnable [pdf, 9pages]
 September 16 (choose one)
R.A. Fisher, The Use of Multiple Measurements in Taxonomic
Problems [pdf, 10pages]
or
J.R. Quinlan, Induction of Decision Trees
[pdf, 26pages]
 September 23
Michael Jordan,
Why the logistic function?
[pdf ,
ps.gz, 13pages]
 September 30
Robert Tibshirani
Regression shrinkage and selection via the lasso
[pdf ,
ps.gz, 28pages]
 Oct 7
Press et al.
Numerical Recipes in C chapters 10.0  10.7 inclusive
[online pdf]
 October 14
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 21
rest of the Mixtures Experts article
 October 28
Geoff Hinton and Radford Neal,
A View of the EM Algorithm, Learning in Graphical Models (1998),
[pdf ,
ps.gz, 14pages]
 November 4 and November 11
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 11
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
[pdf7pages]
 November 18
Golub, Heath, Wahba,
Generalized Cross Validation, Technometrics 1979.
[in class, 9 pages]
 November 25
Rob Shapire, The Strength of Weak Learnability., Machine
Learning 1990.
[pdf31pages]
 December 2
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.
 Some
notes from Geoff Gordon at CMU on SVMs and kernels.
 A short MATLAB tutorial.
Draft Book Chapters
Linear Algebra, v1.3 [ps.gz].
Extra Papers of Interest
 Rob Shapire, Boosting Overview
[ps.gz, 23 pages]
 Leo Brieman, Bagging Predictors
[pdf, 20 pages]
 David Wolpert, Stacked Generalization
[ps.gz, 57 pages]
 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]
 C.K. Chow, C.N. Liu, Approximating Discrete Probability
Distributions with Dependence Trees
[pdf, 6 pages]
 Marina Meila, An accelerated Chow and Liu algorithm
[ps.gz, 12 pages]
 Kwong Yung, Explaining the Stein Paradox
[pdf, 5 pages]
 S. Gallant. Perceptronbased learning algorithms.
[pdf, 13pages]
 Yoav Freund, Rob Schapire, Large Margin Classification Using
the Perceptron Algorithm
[ps.gz, 19pages]
 Pedro Domingos, Michael Pazzani,
On the Optimality of the Simple Bayesian Classifier
[pdf, 28pages]
[
Home 
Course Information 
Lecture Schedule/Notes 
Textbook/Readings 
Assignments/Tests 
Computing 
]
CSC2515  Machine Learning  www.cs.toronto.edu/~roweis/csc2515/
