Professor Harry Wechsler
Department of Computer Science
e-mail : wechsler@cs.gmu.edu
web : http://cs.gmu.edu/~wechsler/
(703) 993-1533 (office)
(703) 993-1530 (sec)
(703)993-1710 (fax)
SPRING '2007
CS
775 --- PATTERN RECOGNITION
001 12181 R 7:20
p.m. – 10:00 p.m. IN 211
(cross
listed with IT 844 --- PATTERN RECOGNITION)
Office Hours
R 6:15 p.m. - 7:00 p.m. or by appointment (SITE
II - Rm. 461)
Textbook
1. C. M. Bishop, Pattern Recognition
and Machine Learning, Springer, 2006.
References
1. V. Cherkassky and
F. Mulier, Learning from Data: Concepts,
Theory, and Methods,
Wiley, 1999.
2. N. Cristianini and J. Shawe-Taylor, An
Introduction to Support Vector Machines,
3. R. Duda, P. Hart
and D. Stork, Pattern Classification, Wiley, 2002.
4. S. Haykin, Neural Networks (2nd ed.),
Prentice-Hall, 1999.
5.
V. Vapnik, The
Nature of Statistical Learning Theory (2nd. ed.), Springer, 2000.
Course
Description
Explores statistical pattern recognition, neural networks,
and statistical learning theory.
Pattern recognition topics include Bayesian classification and decision theory,
density
(parametric and non – parametric) estimation, linear and non – linear
discriminant analysis, dimensionality reduction,
feature extraction and selection,
mixture
models and expectation – maximization (EM), and vector quantization
and
clustering. Neural networks topics include feed-forward networks and
back-propagation,
self-organization feature maps, and radial
basis functions.
Statistical learning theory covers
model selection and support vector machines (SVM).
Course emphasizes experimental
design, applications, and performance evaluation.
Schedule
1st day of classes: January 24,
2007
Spring Break: March 15, 2007
Last Day of Classes: May 3, 2007
Grading
1. Homework
Assignments: 30 %
2. Midterm: 20% <
March 22, 2007>
2. TERM PROJECT: Literature Survey (10%) and Project (40%).