CSCE 478/878 (Fall 2012) lecture slides
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Lectures 0 and 1: Administrivia and Introduction, Aug 20–22, Chapter 1
(pdf6up)
Also see:
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Lecture 2: Concept Learning and the General-to-Specific Ordering,
Aug 22–31, Chapter 2
(pdf6up)
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Lecture 3: Decision Trees, overfitting, Occam's razor,
Sep 3–15, Chapter 3
(pdf6up)
Also see:
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Lecture 4: Artificial Neural Networks and Support Vector Machines
Sep 24–Oct 13, Chapter 4, Section 7.4.4, Littlestone
paper, handout from Christianini and Shawe-Taylor's book
(pdf6up)
Also see:
- Manfred K. Warmuth,
who has done much work on EG and Winnow. Many papers available on-line,
including his GD/EG paper:
- Jyrki Kivinen and
Manfred K. Warmuth.
Exponentiated gradient versus gradient descent for linear predictors.
Information and Computation 132(1):1–64, January 1997.
- Nick Littlestone (creator of Winnow) and some of his papers:
- Avrim Blum and his survey paper
``On-Line Algorithms in Machine Learning''
- ANN growing and pruning:
- SVMs:
- Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B.
An introduction
to kernel-based learning algorithms.
IEEE Transactions on Neural Networks, 12(2):181-201,
2001.
- Nello Cristianini and John Shawe-Taylor.
An Introduction to Support Vector Machines.
Cambridge University Press, 2000.
- Richard Duda,
Peter Hart, and David Stork.
Pattern Classification, 2nd Edition.
John Wiley, 2001.
(Section 5.11)
- SVM tutorial
- KernelMachines.org
- Sebastian Thrun's links
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Lecture 5: Evaluating Hypotheses
Oct 6–Oct 20, Chapter 5 and ROC tutorial
(pdf6up)
Also see:
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Lecture 6: Bayesian learning,
Oct 20–31, Chapter 6
(pdf6up)
Also see:
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Lecture 7: Instance-based learning,
Nov 2–7, Chapter 8
(pdf6up)
Also see:
- Case-based reasoning (thanks to Brandon Hauff):
- David Aha, ed. Lazy Learning. Kluwer Academic Publishers, 1997.
- Christopher M. Bishop.
Neural Networks for Pattern Recognition.
Oxford University Press, 1995. [RBF
networks]
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Sergios Theodoridis and Konstantinos Koutroumbas.
Pattern Recognition.
[distance measures between instances]
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Lecture 7.5: How to give a good research talk
Nov 9–12
(pdf6up)
Also see:
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Lecture 8: Combining Classifiers: Weighted Majority, Bagging and
Boosting,
Nov 14–19, Section 7.5.4, Breiman paper, Freund & Schapire paper
(pdf6up)
Also see:
- Leo Breiman.
Bagging predictors.
Technical Report 421. Dept. of Statistics, University of California-Berkeley.
September 1994.
- Yoav Freund and
Robert Schapire.
A short
introduction to boosting. Journal of Japanese Society for Artificial
Intelligence 14(5):771–780, September, 1999. [Other introductory
and advanced papers are available at Freund's and Schapire's pages.]
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Robert Schapire and Yoav Freund.
Boosting: Foundations and Algorithms.
MIT Press, 2012. ISBN 0-262-01718-0.
- Nick Littlestone and
Manfred K. Warmuth.
The weighted majority algorithm.
Information and Computation 108(2):212–261. February 1994.
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