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
Robinson Hall A243 -- Please note
change of venue -- 7:20 p.m. – 10:00 p.m.
(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.
http://research.microsoft.com/%7Ecmbishop/PRML/
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 (and Tentative List of Topics)
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%).