Bi0034 Analysis and classification of data

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. Ing. Jiří Holčík, CSc. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
At the end of the course, students should be able to: - to know fundamental theoretical and methodological principles of pattern analysis and recognition with emphasis to processing of biological data - explain consequences and relationships between characteristics of real processes and data and applied methods and algorithms; - apply different practical approaches to data processing to obtain required analytic results; - design of modified algorithms to process data of given particular characteristics.
Syllabus
  • Pattern recognition and data classification – basic vocabulary. Sorting of the classification approaches. 2. Classification based on feature description. Classification by means of discriminan functions and minimum distance. 3. Determination of the discriminant function based on statistical characteristics of a set of patterns. 4. Sequential classification. 5. Feature selection and extraction. 6. Principal component analysis. 7. Independent component analysis. 8. Factor analysis. 9. Training of classificators. Methods for estimation of probability density functions and estimation of apriori probabilities of the classification categories. 10. Clustering - principles. Similarity measures. 11. Clustering methods. 12. Neural network classification.
Literature
  • Dunham,M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall 2002
  • Mitchel,T.M.: Machine Learning. McGraw Hill 1997
  • Holčík,J.: Analýza a klasifikace signálů. [Učební texty vysokých škol] Brno, Nakladatelství VUT v Brně 1992.
Teaching methods
Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to be in an interaction with a lecturer.
Assessment methods
oral examination
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.