Title:

# Classification and recognition

Code:KRD
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
CSE-PHD-4DVI4-Elective
Language of Instruction:Czech
Completion:examination
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:390000
ExamsTestsExercisesLaboratoriesOther
Points:1000000
Guarantor:Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Lecturer:Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
Frilecture2019-03-22L220 11:0012:50

Learning objectives:
To understand advanced classification and recognition techniques and to learn how to apply the algorithms and methods to problems in speech recognition, computer graphics and natural language processing. To get acquainted with discriminative training and building hybrid systems.
Description:
Estimation of parameters Maximum Likelihood and Expectation-Maximization, formulation of the objective function of discriminative training, Maximum Mutual information (MMI) criterion, adaptation of GMM models,
transforms of features for recognition, modeling of feature space using discriminative sub-spaces, factor analysis, kernel techniques, calibration and fusion of classifiers, applications in recognition of speech, video and text.
Knowledge and skills required for the course:
Basic knowledge of statistics, probability theory, mathematical analysis and algebra.
Subject specific learning outcomes and competencies:
The students will get acquainted with advanced classification and recognition techniques and learn how to apply basic methods in the fields of speech recognition, computer graphics and natural language processing.
Generic learning outcomes and competencies:
The students will learn to solve general problems of classification and recognition.
Syllabus of lectures:

1. Estimation of parameters of Gaussian probability distribution by Maximum Likelihood (ML)
2. Estimation of parameters of Gaussian Gaussian Mixture Model (GMM) by Expectation-Maximization (EM)
3. Discriminative training, introduction, formulation of the objective function
4. Discriminative training with the Maximum Mutual information (MMI) criterion
5. Adaptation of GMM models- Maximum A-Posteriori (MAP), Maximum Likelihood Linear Regression (MLLR)
6. Transforms of features for recognition - basis, Principal component analysis (PCA)
7. Discriminative transforms of features - Linear Discriminant Analysis (LDA) and Heteroscedastic Linear Discriminant Analysis  (HLDA)
8. Modeling of feature space using discriminative sub-spaces - factor analysis
9. Kernel techniques, SVM
10. Calibration and fusion of classifiers
11. Applications in recognition of speech, video and text
12. Student presentations I
13. Student presentations II

Fundamental literature:

• 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.
Study literature:

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

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