Course detail

Classification and Recognition

ÚSI-2IDKRAcad. year: 2017/2018

The course focuses on the classification and recognition with an accent on statistical methods. Content of the course includes following: The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, Bayes learning, maximum likelihood method, GMM, EM algorithm, discriminative training, kernel methods, hybrid systems, how to merge classifiers, basics of AdaBoost, structural recognition, speech processing applications - speaker recognition, language identification, speech recognition, keyword spotting, image processing - 2D object recognition, face detection, OCR, and natural language processing - document classification, text analysis.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

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.

Prerequisites

There are no prerequisites required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

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.

Assesment methods and criteria linked to learning outcomes

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

Course curriculum

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.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

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

Classification of course in study plans

  • Programme MRzI Master's

    branch RIS , 2. year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Exercise

26 hours, optionally

Teacher / Lecturer