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Lecturer(s)
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Nouza Jan, prof. Ing. CSc.
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Course content
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1. Data processing and visualization in MATLAB 2. Statistical data processing in MATLAB 3. NN and KNN classifier. 4. Etalon based minimum distance classifier. 5. Maximum likelihood classifier. 6. Complete bayesian classifier. 7. Feature selection methods. 8. Data clustering methods. K-Means and LGB algoritms. 9. A* method. 10. Summary.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing)
- Class attendance
- 56 hours per semester
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Learning outcomes
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The course is an introduction to computer decision-making and classification. Students will learn approaches based on the state space theory, feature and syntax based methods to pattern recognition and basic concepts of neural networks. The course is taught in English and is particularly suitable for those who plan stays abroad within the ERASMUS program in further studies.
Basic knowledge of methods related to pattern recognition, classification, decision making, data sorting and clustering.
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Prerequisites
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Condition of registration: basic knowledge of mathematics and statistics. The course is taught by a foreign lecturer, usually during a two-week intensive teaching period planned for the last weeks of the semester. The course is open only if at least 8 students select it. Analysis (functions of one and more variables, search for function extremes) Linear algebra (solution of systems of linear algebraic equations, matrix calculus), Statistics and Probability (Discrete and continuous probability distributions, Bayes' theorem)
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Assessment methods and criteria
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Oral exam, Written exam
Requirements for obtaing the credit are activities at the practicals /seminars.
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Recommended literature
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David G. Stork, Elad Yom-Tov. Computer Manual in MATLAB to Accompany Pattern Classification. 2004.
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Kotek Z., Mařík V., Hlaváč V., Psutka J., Zdráhal Z. Metody rozpoznávání a jejich aplikace.. Academia, Praha, 1993.
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Mařík V., Štěpánková O., Lažanský J. a kol. Umělá inteligence (1). Academia, Praha, 1993.
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Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. 2001.
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