Professor Harry Wechsler
Department of Computer Science
e-mail : email@example.com
(703) 993-1533 (office)
(703) 993-1530 (sec)
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)
R 6:15 p.m. - 7:00 p.m. or by appointment (SITE II - Rm. 461)
1. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
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.
1st day of classes: January 24, 2007
Spring Break: March 15, 2007
Last Day of Classes: May 3, 2007
1. Homework Assignments: 30 %
2. Midterm: 20% < March 22, 2007>
PROJECT: Literature Survey (10%) and