Bi0034 Analysis and classification of biomedical data

Faculty of Science
Autumn 2008
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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.
Timetable
Tue 12:00–13:50 MP2,01014a
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
The course provides students with fundamental methods and algorithms for description, analysis and classification of biomedical data. It deals with basic approaches of pattern recognition (decision-theoretic and structural approaches) and describes principles of both the approaches. Further, it discusses methods of the decision-theoretic approach in details. Classification using discrimination functions and minimum distance. Sequential classification. Choise and selection of features. Principal component analysis. Independent component analysis. Factor analysis. Classificator training. Clustering. Similarit measures. Neural networks and their application in pattern recognition. At the end of the course, students should be able to understand, design and implement the described algorithms for pattern recognition and decision-making.
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
  • Mitchel,T.M.: Machine Learning. McGraw Hill 1997
  • 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.
Assessment methods
oral examination
Language of instruction
Czech
Further Comments
Study Materials
The course is taught last offered.
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 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.
  • Enrolment Statistics (Autumn 2008, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2008/Bi0034