Bi0034 Analysis and classification of data

Faculty of Science
Autumn 2012
Extent and Intensity
2/1. 3 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)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Terézia Černá (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer), RNDr. Eva Koriťáková, Ph.D. (deputy)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. Ing. Jiří Holčík, CSc.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 8:00–10:50 F01B1/709
Course Enrolment Limitations
The course is offered to students of any study field.
Abstract
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.
Key topics
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.
Study resources and 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.
Approaches, practices, and methods used in teaching
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.
Method of verifying learning outcomes and course completion requirements
oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (Autumn 2012, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2012/Bi0034