Pattern Recognition

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Theoretical Computer Science

Synopsis:

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.

Requirements:

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.

Note:

Further information:

https://courses.fit.cvut.cz/MI-ROZ/

No time-table has been prepared for this course

The course is a part of the following study plans:

• Master branch Knowledge Engineering, in Czech, 2016-2017 (elective course)
• Master branch Computer Security, in Czech, 2016-2019 (elective course)
• Master branch Computer Systems and Networks, in Czech, 2016-2019 (elective course)
• Master branch Design and Programming of Embedded Systems, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Info. Systems and Management, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Software Engineering, in Czech, 2016-2019 (elective course)
• Master branch Web and Software Engineering, spec. Web Engineering, in Czech, 2016-2019 (elective course)
• Master program Informatics, unspecified branch, in Czech, version 2016-2019 (elective course)
• Master branch System Programming, spec. System Programming, in Czech, 2016-2019 (elective course)
• Master branch System Programming, spec. Computer Science, in Czech, 2016-2017 (elective course)
• Master specialization Computer Science, in Czech, 2018-2019 (elective course)
• Master branch Knowledge Engineering, in Czech, 2018-2019 (elective course)