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Lecturer(s)
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Hron Karel, prof. RNDr. Ph.D.
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Fačevicová Kamila, Mgr. Ph.D.
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Course content
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1. Basics of statistical learning 2. Linear regression and classification 3. Resampling methods - cross validation and bootstrap 4. Linear model selection and regularization 5. Beyond linearity - splines, generalized additive models 6. Functional data analysis - goals and methods 7. Functional principal component analysis 8. Functional regression
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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 Exam
- 40 hours per semester
- Preparation for the Course Credit
- 20 hours per semester
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Learning outcomes
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Understand popular advanced methods of statistical learning including their implementation in statistical software R.
Application Apply probability theory and multivariate statistics to methods of statistical learning.
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Prerequisites
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Basic knowledge of probability theory and multivariate statistics.
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Assessment methods and criteria
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Oral exam, Seminar Work
Credit: Presetation of a project covering topics of statistical learning. Exam: oral.
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Recommended literature
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B. Efron, R. Hastie. (2017). Computer age statistical inference. Cambridge University Press, Cambridge.
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B. Everitt, T. Hothorn. (2011). An introduction to applied multivariate analysis with R. Springer, Heidelberg.
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G. James, D. Witten, T. Hastie, R. Tibshirani. (2014). An introduction to statistical learning. Springer, New York.
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J.O Ramsay, B.W. Silverman. (2005). Functional data analysis. Springer, New York.
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T. Hastie, R. Tibshirani, J. Friedman. (2016). The elements of statistical learning. Springer, New York.
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