CSC2515 Fall 2002 - Weekly and Other Readings
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.
- 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]
J.R. Quinlan, Induction of Decision Trees
- September 25
Why the logistic function?
- October 2
Regression shrinkage and selection via the lasso
- 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
Note: only read pages 1-10 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),
- November 6 and November 13
Sam Roweis and Zoubin Ghahramani,
A Unifying Review of Linear Gaussian Models,
Neural Compuation (1999),
Skip sections 5.4, 6.2, 8, 9, A.3
- November 13
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
- 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): 273-297 (1995)
- Probability and Statistics Review
- A tutorial
by Andrew Moore at CMU on Bayesian methods.
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
- 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].
Course Information |
Lecture Schedule/Notes |
CSC2515 - Machine Learning || www.cs.toronto.edu/~roweis/csc2515/