Czech

Not applicable.

The students will get acquainted with classification and recognition techniques and learn how to apply basic algorithms and methods in the fields of speech recognition, computer graphics and natural language processing. They will understand the common aspects and differences of the particular methods and will be able to take advantage of the existing classifiers in real-life projects.

There are no prerequisites required.

Not applicable.

Teaching is carried out through lectures and seminars. Lectures consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions. Seminars particularly support practical mastery of subject matter presented in lectures or assigned for individual study with the active participation of students.

Mid-term test – max. 15 points; individual project – max. 25 points; the final written exam – max. 60 points.

1. The tasks of classification and pattern recognition, basic schema of a classifier, data sets and evaluation.
2. Probabilistic distributions and linear models.
3. Statistical pattern recognition, Bayes learning, maximum likelihood method.
4. Sequential data modelling, hidden Markov models, linear dynamical systems.
5. Generative and discriminative models.
6. Speech processing applications - speaker recognition, language identification, speech recognition, keyword spotting.
7. Kernel methods.
8. Mixture models, EM algorithm.
9. Combining models, boosting.
10. AdaBoost, basics and extensions of the model (method).
11. Image processing - 2D object recognition, face detection, OCR.
12. Pattern recognition in text, grammars, languages, text analysis.
13. Project presentation, directions for further development.

Not applicable.

To understand the foundations of classification and recognition and to learn how to apply basic algorithms and methods in this fields: speech recognition, computer graphics and natural language processing. To get acquainted with the evaluation of the success of procedures. To conceive basics of statistical pattern recognition, discriminative training and building hybrid systems.

Not applicable.

Not applicable.

Not applicable.

Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.

Fukunaga, K. Statistical pattern recognition, Morgan Kaufmann, 1990, ISBN 0-122-69851-7.
Mařík, V., Štěpánková, O., Lažanský, J. a kol.: Umělá inteligence (1-4), ACADEMIA Praha, 1998-2003, ISBN 80-200-1044-0.