Professor Harry Wechsler

Department of Computer Science

George Mason University

Fairfax, VA 22030

e-mail :

web :

(703) 993-1533 (office)

(703) 993-1530 (sec)

(703)993-1710 (fax)



SPRING '2007



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)


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, Cambridge University Press, 2001.


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>

2. TERM PROJECT: Literature Survey (10%) and Project (40%).