CSCE 478/878 (Fall 2008) lecture slides
-
Lectures 0 and 1: Administrivia and Introduction, Aug 25–29, Chapter 1
(pdf6up)
Also see:
- Labor Day Sep 1; no
class
- Prerequisite test due Sep 3
-
Lecture 2: Concept Learning and the General-to-Specific Ordering,
Sep 3–8, Chapter 2
(pdf1up, pdf6up)
Topic summary 1 due Wednesday, Sept 17
-
Lecture 3: Decision Trees, overfitting, Occam's razor,
Sep 10–22, Chapter 3
(pdf1up,
pdf6up)
Topic summary 2 due Monday, Sep 29
Also see:
- Homework 0 due Sep 15
- Homework 1
Assigned Monday, September 22;
Due
Friday, October 10
Sunday, October 12
at 11:59 p.m.
-
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
(pdf1up,
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:
- Nick Littlestone (creator of Winnow) and some of his papers:
- 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]
(pdf4up)
- 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
-
Lecture 5: Evaluating Hypotheses
Oct 17–Oct 31, Chapter 5 and ROC tutorial
(pdf1up,
pdf6up)
Also see:
- Fall break, Oct 20,
no class
-
Lecture 6: Bayesian learning,
Oct 27–Nov 12, Chapter 6
(pdf1up,
pdf6up)
Also see:
-
Lecture 7: Instance-based learning,
Nov 14–21, Chapter 8
(pdf1up,
pdf4up)
Also see:
-
Lecture 8.5: How to give a good research talk,
Nov 21,
(pdf1up,
pdf6up)
Also see:
-
Lecture 8: Combining Classifiers: Weighted Majority, Bagging and
Boosting,
Nov 24–Dec 5, Section 7.5.4, Breiman paper, Freund & Schapire paper
(pdf1up,
pdf4up)
Also see:
Thanksgiving holiday, Nov 26–28, no class
Lecture 9: Students' choice, if time permits
Project presentations, Dec 8–12 (dead week)
Project reports due Dec 10
Return to the CSCE 478/878 (Fall 2008) Home Page
Last modified 16 August 2011; please report problems to
sscott.