Pattern Recognition

Lecturer:

Michal Haindl (gar.)

Tutor:

Michal Haindl (gar.), Mikuláš Krupička

Supervisor:

Department of 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:

Time-table for winter semester 2011/2012:

	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
Fri
Thu	roomT9:349 Haindl M. 07:30–09:00 (lecture parallel1) Dejvice NBFIT místnostroomT9:351 Krupička M. 09:15–10:45 EVEN WEEK (lecture parallel1 parallel nr.101) Dejvice NBFIT PC ucebna
Fri

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Knowledge Engineering, Version for Students who Enrolled in 2010, Presented in Czech (compulsory course of the specialization)
• Master Informatics, Version for Students who Enrolled in 2010, Presented in Czech (VO)
• Master Informatics, Presented in Czech, Version for Students who Enrolled in 2011 (VO)
• Knowledge Engineering, Presented in Czech, Version for Students who Enrolled in 2011 (compulsory course of the specialization)
• Master Informatics, Presented in Czech, Version for Students who Enrolled in 2012 (VO)
• Knowledge Engineering, in Czech, Presented in Czech, Version for Students who Enrolled in 2012 (compulsory course of the specialization)