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Lecturer(s)
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Hron Karel, prof. RNDr. Ph.D., DSc.
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Fačevicová Kamila, Mgr. Ph.D.
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Czolková Adéla, Bc.
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Course content
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1. Multidimensional data and their visualization, preprocessing of multidimensional data 2. Fundamentals of robust statistics 3. Data dimension reduction - principal component method, factor analysis and related methods 4. Regression for high-dimensional data (PCR, PLS regression and their alternatives) 5. Classification - LDA, logistic regression, kNN and related methods 6. Evaluation of regression and classification models 7. Cluster analysis - hierarchical clustering, k-means method, model clustering and their alternatives. 8. Advanced methods and complex analysis of multidimensional data
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Attendace
- 52 hours per semester
- Preparation for the Course Credit
- 20 hours per semester
- Preparation for the Exam
- 30 hours per semester
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Learning outcomes
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Understand basic methods of multivariate statistical analysis including their implementation in statistical software R. Active participation.
Application Apply probability theory to methods of multivariate statistical analysis.
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Prerequisites
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Basic knowledge of probability theory and mathematical statistics.
KMA/PST
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Assessment methods and criteria
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Oral exam
Credit: comprehensive statistical processing of a data set, presentation of results. Exam: oral.
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Recommended literature
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B. Everitt, T. Hothorn. (2011). An introduction to applied multivariate analysis with R. Heidelberg.
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G. James, D. Witten, T. Hastie, R. Tibshirani. (2014). An introduction to statistical learning, corr. 4th printing. New York.
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K. Varmuza, P. Filzmoser. (2008). Introduction to multivariate statistical analysis in chemometrics. CRC Press.
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Meloun, M., Milirký, J., Hill, M. (2017). Statistická analýza vícerozměrných dat v příkladech. Praha.
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Murphy, K. P. (2022). Probabilistic machine learning, An introduction. Cambridge.
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R. Maronna, R. D., Martin, V.J. Yohai. (2006). Robust statistics: Theory and methods. New York.
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R. Wehrens. (2011). Chemometrics with R. Heidelberg.
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