CSC2515 Fall 2004 - 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 14
L.G. Valiant, A Theory of the Learnable [pdf, 9pages]
- September 21
R.A. Fisher, The Use of Multiple Measurements in Taxonomic
Problems [pdf, 10pages]
- September 28
Why the logistic function?
- October 5
Regression shrinkage and selection via the lasso
- October 12
Rumelhart, Hinton and Williams,
Learning representation by backpropagating errors,
- October 19
Michael I. Jordan and Robert A. Jacobs (1994),
Hierarchical Mixtures of Experts and the EM Algorithm
- October 26
C.K.Chow and C.N. Liu,
Approximating discrete probability distributions with dependence trees
[pdf, 6 pages]
- November 2
Geoff Hinton and Radford Neal,
A View of the EM Algorithm, Learning in Graphical Models (1998),
- November 9
Zoubin Ghahramani and Geoff Hinton,
The EM algorithm for Mixtures of Factor Analyzers
[ps.gz, 8 pages]
- November 16
Alan Poritz, Hidden Markov Models: A guided tour., ICASSP 1988.
- November 23
Bradley Efron and Gail Gong,
A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
The American Statistician, Vol. 37, No. 1. (Feb., 1983), pp. 36-48.
(Note that there is a tiny typo in this paper: 2 lines below
expression (3) on the 1st page, the bar is ommited from the x(i) on
the right side of the equation.)
- November 30
Rob Shapire, The Strength of Weak Learnability., Machine
- December 7
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.
from Numerical Recipes in C talks about linear programming.
You might also be interested in the
other material, especially from Chapter 10 and 15.
Draft Book Chapters
Linear Algebra, v1.3 [ps.gz].
Extra Papers of Interest
- Boser, Guyon, Vapnik, A Training Algorithm for Optimal Margin
Classifiers, COLT 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]
- Marina Meila, An accelerated Chow and Liu algorithm
[ps.gz, 12 pages]
- 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.
Course Information |
Lecture Schedule/Notes |
CSC2515 - Machine Learning || www.cs.toronto.edu/~roweis/csc2515/