Statistical Pattern Recognition - N445088
Title: Statistické rozpoznávání
Guaranteed by: Department of Computing and Control Engineering (445)
Faculty: Faculty of Chemical Engineering
Actual: from 2021
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: cancelled
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: Kukal Jaromír doc. Ing. Ph.D.
Is interchangeable with: M445021
Examination dates   Schedule   
Annotation -
The lectures are oriented to application of mathematical statistics in pattern recognition. Basic terms, their relationships to statistics, programming techniques, and descriptor design are poined out together with research, real data processing, and applications.
Last update: TAJ445 (14.12.2013)
Aim of the course -

Students will be able to:

Design descriptors for statistical pattern recognition

Apply data transforms for improvement of pattern recognition quality

Apply standard pattern recognition methods

Evaluate quality of pattern recognition systém

Last update: Kukal Jaromír (11.09.2013)
Literature -

R:Fukunaga K., Introduction to Statistical Pattern Recognition, Academic Press, London, 1990

R:Shawe-Taylor J., Cristianini N., Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2009

A:Scholkopf B., Smola A.J., Learning with Kernels, MIT Press, Cambridge, 2002

Last update: TAJ445 (30.09.2013)
Learning resources -

interal training materials

Last update: Kukal Jaromír (11.09.2013)
Syllabus -

1. Class, pattern, descriptors, pattern set for statistical pattern recognition

2. Repetitorium of basic statistical terms and principles

3. Quality of desroptor set: the best descriptor, acceptable subset of descriptors

4. Linear discrimination analysis as a tool for pattern recognition

5. Evaluation of pattern recognition quality: p-value, sensitivity, specificity, error, AIC, BIC

6. Cross-validation methodology in evaluation of recognition quality

7. Linear data transforms: normaliztion, standardization, PCA, spherization

8. Robust and regularized methods and their advantages in pattern recognition

9. Application of metrics in pattern recognition: Euclidean, Minkowski, Mahalanobis, k-NN, c-mean

10. Application of PDF in pattern recognition: GMM, Parzen and LQ estimates

11. Linear, non-linear, and logistic regression as pattern recognition tools

12. Feature space reduction as application of binary optimization

13. Kernel functions in design of non-linear classifiers

14. Fuzzy sets in pattern recognition: fuzzification, FCM

Last update: Kukal Jaromír (11.09.2013)
Entry requirements -

Basic knowledge of mathematical statistics and programming in MATLAB.

Last update: Kukal Jaromír (11.09.2013)
Registration requirements -


Last update: TAJ445 (30.09.2013)
Teaching methods
Activity Credits Hours
Konzultace s vyučujícími 0.5 14
Účast na přednáškách 1 28
Práce na individuálním projektu 2 56
Příprava na zkoušku a její absolvování 0.5 14
Účast na seminářích 1 28
5 / 5 140 / 140
Coursework assessment
Form Significance
Regular attendance 40
Oral examination 60