CSC2515 Fall 2003 - Weekly and Other Readings
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.
- 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]
J.R. Quinlan, Induction of Decision Trees
- September 23
Why the logistic function?
- September 30
Regression shrinkage and selection via the lasso
- Oct 7
Press et al.
Numerical Recipes in C chapters 10.0 - 10.7 inclusive
- October 14
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 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),
- November 4 and November 11
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 11
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
- November 18
Golub, Heath, Wahba,
Generalized Cross Validation, Technometrics 1979.
[in class, 9 pages]
- November 25
Rob Shapire, The Strength of Weak Learnability., Machine
- December 2
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.
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. Perceptron-based learning algorithms.
- Yoav Freund, Rob Schapire, Large Margin Classification Using
the Perceptron Algorithm
- Pedro Domingos, Michael Pazzani,
On the Optimality of the Simple Bayesian Classifier
Course Information |
Lecture Schedule/Notes |
CSC2515 - Machine Learning || www.cs.toronto.edu/~roweis/csc2515/