Description:

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.

Contents:

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.

Seminar contents:

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.

Recommended literature:

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.

Keywords:

pattern recognition, classification, clustering, feature selection / extraction, learning

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar