CSCE 478/878 (Fall 2003) lecture slides
Slides for some dates are not yet prepared, so the links will
not work. See the slides
from the Fall 2001
offering to see early versions of those slides.
-
Lectures 0 and 1: Administrivia and Introduction, Aug 25-27, Chapter 1
(ps1up,
pdf1up,
ps4up,
pdf4up)
- Homework 0 due Aug 29
- Labor Day, Sep 1, no class
- Prerequisite test due Sep 5
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Lecture 2: Concept Learning and the General-to-Specific Ordering,
Sep 3-8, Chapter 2
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 1 due Monday, Sept 15
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Lecture 3: Computational Learning Theory,
Sep 8-17, Sections 7.1-7.4.3, 7.5-7.5.3, 7.6
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 2 due Wednesday, Sept 24
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Lecture 4: Decision Trees, overfitting, Occam's razor,
Sep 17-24, Chapter 3
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 3 due Wednesday, Oct 8
Also see:
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Lecture 5: Artificial Neural Networks and Support Vector Machines
Sep 29-Oct 29, Chapter 4, Section 7.4.4, Littlestone
paper, handout from Christianini and Shawe-Taylor's book
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 4 due Wednesday, Nov 5
Also see:
- Manfred K. Warmuth,
who has done much work on EG and Winnow. Many papers available on-line,
including his GD/EG paper:
- 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.
- Christopher Burges.
A
tutorial on support vector machines for pattern recognition
- 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.
[also see software supplements]
John Wiley, 2001.
(Section 5.11)
- SVM tutorial
- KernelMachines.org
- Sebastian Thrun's links
- Fall break, Oct 20, no class
-
Lecture 6: Evaluating Hypotheses
Nov 3-5, Chapter 5
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 5 due Wednesday, Nov 12
Also see:
-
Lecture 7: Combining Classifiers: Weighted Majority, Bagging and
Boosting,
Nov 10-12, Section 7.5.4, Breiman paper, Freund & Schapire paper
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 6 due Wednesday, Nov 19
Also see:
-
Lecture 8: Bayesian learning,
Nov 17-24, Chapter 6
(ps1up,
pdf1up,
ps4up,
pdf4up)
Also see:
- Thanksgiving holiday, Nov 26, no class
-
Lecture 8.5: How to give a good research talk,
Dec 1, Goldman slides
(ps4up,
pdf4up, pdf1up)
Also see:
- Chris Hammack's thesis defense on LASSO, Wednesday, Dec 3,
11:30a.m. in Ferguson 114
-
Lecture 9: Instance-based learning,
Dec 3, Chapter 8
(ps1up,
pdf1up,
ps4up,
pdf4up)
Also see:
-
Lecture 10: Reinforcement learning,
Dec 3, Chapter 13
(ps1up,
pdf1up,
ps4up,
pdf4up)
Also see:
- Course evaluations
(ps, pdf), Dec 3
-
Project presentations, Dec 8-10
(dead week)
- Project reports due Dec 12
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Last modified 16 August 2011; please report problems to
sscott AT cse.