Bi0034 Analysis and classification of data

Faculty of Science
autumn 2017
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
prof. Ing. Jiří Holčík, CSc. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Eva Koriťáková, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 18. 9. to Fri 15. 12. Wed 8:00–10:50 D29/347-RCX2
Prerequisites
It is recommended to attend the course Bi8600 Multivariate Methods beforehand.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
At the end of the course, students should be able to: - know fundamental theoretical and methodological principles of pattern analysis and recognition with emphasis to processing of medical and biological data;- choose and apply appropriatedata analysis method to obtain required results; - reduce and transform multivariate data using advances ordination methods; - classify data using various classification methods; - design modified algorithms to process data of given particular characteristics; - interpret obtained results including evaluation of classification performance.
Syllabus
  • 1. Multivariate data analysis and its aims. 2. Ordination methods – factor analysis (FA). 3. Ordination methods – canonical correspondence analysis (CCA), redundancy analysis (RDA). 4. Ordination methods – co-coordinate analysis. 5. Advanced method of feature extraction – independent component analysis (ICA) and other advanced methods. 6. Distance and similarity metrics. 7. Classification – introduction, discriminant analysis based on discriminant functions and minimal distance. 8. Classification – discriminant analysis based on boundaries: Fisher’s linear discriminant analysis. 9. Classification – discriminant analysis based on boundaries: linear support vector machines (SVM). 10. Classification – nonlinearSVM, sequential classification. 11. Evaluation of classification performance – cross-validation, comparison of classification results with random classification, comparison of classification performance of two or more classifiers. 12. Summary of methods for data analysis and classification.
Literature
  • Dunham,M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall 2002
  • Holčík,J.: Analýza a klasifikace signálů. [Učební texty vysokých škol] Brno, Nakladatelství VUT v Brně 1992.
  • Mitchel,T.M.: Machine Learning. McGraw Hill 1997
Teaching methods
Lectures supported by Power Point presentations. Understanding of principles, methods and algorithms is emphasized. Students are continuously encouraged to interact with the lecturer.
Assessment methods
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 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (autumn 2017, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2017/Bi0034