Pattern Recognition (fall 2007)

The course is for 3rd year undergraduates and graduate students of CS. We will study general principles of Statistical Learning Theory as well as different frameworks for solving Pattern Recognition (PR) problem on the base of Training Sample Set (TSS). Special emphasis is given to a new promising PR structure, called Support Vector Machine. Different methods of Cluster Analysis and Features Selection are also considered in the course.

Course plan:

Assignements: 6 homeworks with exercises. At the end of the course the students should pass exams in the form of a special homework (during three days). The final mark is mostly based on the result of this final homework. The results of mentioned 6 homeworks also have an influence on the final mark.

Requirements: Standard courses on Mathematics (Probability and Statistics, Linear Algebra, Infi).


R. Duda, P. Hart, D. Strok Pattern Classification, Wiley, 2001.

S. Theodoridis, K. Koutroumbas, Pattern Recognition, Third Edition, Academic Press, 2006.

P.J. Rousseeuv and A.M. Leroy, Robust Regression and Outlier Detection' Wiley, 1987.

L. Breiman et others, Classification And Regression Trees, Wadsworth Int. Group, 1984.

A.K. Jain, R.C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.

V.N. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.