Course detail
Multidimensional Analysis of Biomedical Data
FEKT-MPA-VMMAcad. year: 2020/2021
Not applicable.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
2. Linear algebra foundations.
3. Multidimentional distributions and statistical tests.
4. Methods for data preprocessing. Transformation and standardization approaches. Problem of missing data.
5. Relationship between variables in multidimentional space. Similarity and distance measures. Correlation and covariance.
6. Cluster analysis of biological data. Hierarchical and non-hierarchical clustering. Determining the optimal number of clusters. Clusters validation.
7. Ordinal analysis. Review of the methods used in biomedical applications.
8. Principal component analysis (PCA). Singular value decomposition.
9. Factor analysis. Fundamentals of factor analysis. Rotation of the factors.
10. Independent component analysis (ICA). ICA based feature extraction from biomedical data. Relationship between PCA, ICA and factor analysis.
11. Non-linear methods for data dimensionality reduction.
12. Multidimensional data analysis in biomedicine applications – overview.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
J.H. McDonald: Handbook of Biological Statistics, Sparky House Publishing, 2008 (CS)
Recommended reading
Classification of course in study plans
- Programme MPA-BIO Master's 1 year of study, winter semester, compulsory