Pattern Recognition 1

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

Elements of pattern recognition. Basic PR concepts. Bayesian decision theory. Learning theory. Parametric classifiers. Context classifiers. Classification quality estimation. Vector support machines. Non-parametric classifiers. Feature selection. Cluster analysis.

Requirements:

probability, statistics, mathematical analysis, algebra, programming

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

The main aim of the course is to give a systematic account of the major topics in pattern recognition with emphasis on problems and applications of the statistical approach to pattern recognition. Basic concepts and methods of pattern recognition, incl. machine perception, probability models and computations, parameter estimation will be instructed.

Study materials:

A. Webb, Statistical Pattern Recognition, J. Wiley, 2002.

P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall,1982. ,

R. Duda and P. Hart and D.G. Stork, Pattern Classification, J. Wiley, 2001.

Theodoridis, S. and Koutroumbas, K., Pattern Recognition, Elsevier, 2003.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: