Pattern Recognition

Course guarantor:

Michal Haindl

Lecturer:

Michal Haindl

Tutor:

Michal Haindl, Radek Richtr

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/

Time-table for winter semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed
Thu	roomTH:A-1242 Haindl M. 09:15–10:45 (lecture parallel1) Thákurova 7 (budova FSv)roomTH:A-1242 Richtr R. 11:00–12:30 ODD WEEK (lecture parallel1 parallel nr.101) Thákurova 7 (budova FSv)
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Master specialization Computer Security, in Czech, 2020 (elective course)
• Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 202 (elective course)
• Master specialization Management Informatics, in Czech, 2020 (elective course)
• Master specialization Software Engineering, in Czech, 2020 (elective course)
• Master specialization System Programming, in Czech, version from 2020 (elective course)
• Master specialization Web Engineering, in Czech, 2020 (elective course)
• Master specialization Knowledge Engineering, in Czech, 2020 (elective course)
• Master specialization Computer Science, in Czech, 2020 (elective course)
• Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (elective course)
• Study plan for Ukrainian refugees (elective course)
• Master specialization System Programming, in Czech, version from 2023 (elective course)
• Master specialization Computer Science, in Czech, 2023 (elective course)