CSCE 478/878 (Fall 2001) lecture slides
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Lectures 0 and 1: Administrivia and Introduction, Aug 27-29, Chapter 1
(ps1up,
pdf1up,
ps4up,
pdf4up)
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Lecture 2: Concept Learning and the General-to-Specific Ordering,
Sep 5-10, Chapter 2
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 1 due Monday, Sept 17
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Lecture 3: Computational Learning Theory,
Sep 10-19, Sections 7.1-7.4.3, 7.5-7.5.3, 7.6
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 2 due Wednesday, Sept 26
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Lecture 4: Decision Trees, overfitting, Occam's razor,
Sep 19-26, Chapter 3
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 3 due Wednesday, Oct 3
Topic summary 3 now due Friday, Oct 5
Also see:
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Lecture 5: Artificial Neural Networks
Oct 1-15, Chapter 4, Section 4.4, Littlestone paper
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 4 due Wednesday, Oct 24
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:
- Nick Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2:285-318, 1988.
- Nick Littlestone. Redundant noisy attributes, attribute errors, and
linear threshold learning using Winnow. In
Proc. 4th Annu. Workshop on Comput. Learning Theory,
147-156, 1991. Morgan Kaufmann.
- Avrim Blum and his survey paper
``On-Line Algorithms in Machine Learning''
- Notes from CSCE 970 on nonlinear classifiers [on the intuition behind
using multiple layers to remap the input space]
(ps4up,
pdf4up)
- ANN growing and pruning:
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Lecture 6: Evaluating Hypotheses
Oct 17-24, Chapter 5
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 5 due Wednesday, Oct 31
Also see:
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Lecture 7: Combining Classifiers: Weighted Majority, Bagging and
Boosting,
Oct 29-31, Section 7.5.4, Breiman paper, Freund & Schapire paper
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 6 due Wednesday, Nov 7
Also see:
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Special Event: Colloquium by Pat Langley on
adaptive user interfaces, scheduled for Monday, November 5, 2:30pm in
CBA 25.
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Lecture 8: Bayesian learning,
Nov 5-Nov 14, Chapter 6
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 7 due Monday, Nov 26
Also see:
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Lecture 9: Instance-based learning,
Nov 14-26, Chapter 8
(ps1up,
pdf1up,
ps4up,
pdf4up)
Topic summary 8 tentatively due Monday, Dec 3
Also see:
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Lecture 9.5: How to give a good research talk,
Nov 28, Goldman slides
(ps4up,
pdf4up)
Also see:
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Lecture 10: Reinforcement learning,
Nov 26-Dec 3, Chapter 13
(ps1up,
pdf1up,
ps4up,
pdf4up)
Also see:
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Lecture 11: Genetic Algorithms,
Dec 3, Chapter 9
(ps1up,
pdf1up,
ps4up,
pdf4up)
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Special lecture by Cory Strope on a multiple-instance learning
algorithm, and course evaluations
(ps, pdf), Dec 5
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Project presentations, Dec 10-12
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Special Event: Colloquium by Sally Goldman on
multiple-instance learning, scheduled for Thursday, December
13, 3:30pm [refreshments in
114 Ferguson], 3:45pm [talk in 217 Ferguson] (abstract,
colloquium
schedule)
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