Pattern Recognition

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Code Completion Credits Range Language
NI-ROZ Z,ZK 5 2P+1C Czech
Garant předmětu:
Michal Haindl
Michal Haindl
Michal Haindl, Radek Richtr
Department of Theoretical Computer Science

The aim of the module 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. Students will learn the fundamental concepts and methods of pattern recognition, including probability models, parameter estimation, and their numerical aspects.


introductory probability, programming, English

Syllabus of lectures:

1. Elements of pattern recognition.

2. Basic pattern recognition concepts.

3. Bayesian decision theory.

4. Learning theory.

5. Parametric classifiers.

6. Non-parametric classifiers.

7. Support vector machines.

8. Hierarchical classifiers.

9. Pattern recognition using neural networks.

10. Classification quality estimation.

11. Dimensionality reduction.

12. Feature selection.

13. Cluster analysis.

Syllabus of tutorials:

1. Course project assignment.

2. Consultations.

3. Consultations.

4. Consultations.

5. Consultations.

6. Course project control.

7. Consultations.

8. Consultations.

9. Consultations.

10. Consultations.

11. Consultations.

12. Projects presentation workshop.

13. Projects presentation workshop, assessment.

Study Objective:

Pattern Recognition is the prerequisite for modern approaches to artificial intelligence, machine perception, computer graphics, and many other related disciplines, such as date mining, hypermedia, etc. Students will learn elements of pattern recognition, Bayesian decision theory, learning theory, parametric and non-parametric classifiers, support vector machines, classification quality estimations, feature selection, and cluster analysis.

Study materials:

1. Devijver, P. A., Kittler, J. ''Pattern Recognition: A Statistical Approach''. Prentice Hall, 1982. ISBN 0136542360.

2. Duda, R. O., Hart, P. E., Stork, D. G. ''Pattern Classification (2nd Edition)''. Wiley-Interscience, 2000. ISBN 0471056693.

3. Webb, A. R. ''Statistical Pattern Recognition (2nd Edition)''. Wiley, 2002. ISBN 0470845147.

4. Theodoridis, S., Koutroumbas, K. ''Pattern Recognition''. Academic Press, 2008. ISBN 1597492728.

Further information:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-06-16
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