CSC2515 Fall 2005 - Weekly and Other Readings
There is no required textbook for the class.
Two recommended books that cover similar material are
Hastie, Tibshirani, Friedman
Elements of Statistical Learning and
Info Theory, Inference, and Learning Algorithms which is freely
I will 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 13
L.G. Valiant, A Theory of the Learnable [pdf, 9pages]
- September 20
R.A. Fisher, The Use of Multiple Measurements in Taxonomic
Problems [pdf, 10pages]
- September 26
Pedro Domingos, Michael Pazzani,
On the Optimality of the Simple Bayesian Classifier
- October 4
Why the logistic function?
- October 11
Rumelhart, Hinton and Williams,
Learning representation by backpropagating errors,
- October 18
Michael I. Jordan and Robert A. Jacobs (1994),
Hierarchical Mixtures of Experts and the EM Algorithm
- October 25
C.K.Chow and C.N. Liu,
Approximating discrete probability distributions with dependence trees
[pdf, 6 pages]
- November 1
Geoff Hinton and Radford Neal,
A View of the EM Algorithm, Learning in Graphical Models (1998),
- November 8
Zoubin Ghahramani and Geoff Hinton,
The EM algorithm for Mixtures of Factor Analyzers
pdf, 8 pages]
- November 15
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
- November 22
Lawrence Saul, Fernando Pereira, Aggregate and mixed-order Markov
models for statistical language processing., EMNLP 1997.
- November 29
Rob Shapire, The Strength of Weak Learnability., Machine
- December 6
Corinna Cortes and Vladimir Vapnik, Support Vector Networks,
Machine Learning 20(3): 273-297 (1995)
- Probability and Statistics Review
- Some useful
matrix identities and
Andrew Moore at CMU
tutorials page with many excellent mini-tutorials on
various statistical machine learning topics.
In particular, you might want to check out
his tutorials on probability and density, and
on Gaussian and Bayesian classifiers.
- A short MATLAB tutorial.
- Numerical Recipes in C has some useful things on optimization and computing
with real numbers, although some of their algorithms I wouldn't
recommend actually using. It is available in pdf on the web
Chapters 10 and and 15 might be of interest.
Extra Papers of Interest
- Geman, Bienstock, Doursat, Neural Networks and the
Neural Computation (1992),
- Sam Roweis and Zoubin Ghahramani,
A Unifying Review of Linear Gaussian Models,
Neural Compuation (1999),
- David Mackay, Maximum Likelihood and Covariant Algorithms for
ICA [ps.gz, 15 pages]
- Joel Max, Quantizing for Minimum Distortion, IRE
Transactions on Information Theory, March 1960.
[pdf, 6 pages]
- Stuart Lloyd, Least Squares Quantization in PCM, IEEE
Transactions on Information Theory 28(2), March 1982.
[pdf, 9 pages]
- Fix, Hodges, Nonparametric Discrimination: Consistency
Properties, 1951. [pdf, 21 pages]
- Marina Meila, An accelerated Chow and Liu algorithm
[ps.gz, 12 pages]
- Robert Tibshirani
Regression shrinkage and selection via the lasso
- An article from Scientific American
on Stein's Paradox. Another paper on
- Golub, Heath, Wahaba,
Generalized Cross Validation, Technometrics 1979.
- The voted perceptron algorithm is introduced in
this paper by Freund and Schapire.
- The very cool Winnow algorithm
is introduced by Littlestone in this paper.
- Some papers on decision trees:
Quinlan's original paper,
a review paper by Murthy,
a paper by Chou on approximately
- A couple of suggestions for how to
improve Fisher's discriminant.
Course Information |
Lecture Schedule/Notes |
CSC2515 - Machine Learning || www.cs.toronto.edu/~roweis/csc2515/