|
Lecturer(s)
|
-
Fačevicová Kamila, Mgr. Ph.D.
-
Hron Karel, prof. RNDr. Ph.D.
|
|
Course content
|
1. Basics of statistical learning 2. Linear regression 3. Classification 4. Resampling methods - cross validation and bootstrap 5. Linear model selection and regularization 6. Beyond linearity - splines, generalized additive models 7. Deep learning 7. Survival analysis 8. Unsupervised learning (PCA, cluster analysis)
|
|
Learning activities and teaching methods
|
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Preparation for the Exam
- 40 hours per semester
- Preparation for the Course Credit
- 20 hours per semester
- Attendace
- 52 hours per semester
|
|
Learning outcomes
|
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.
|
|
Prerequisites
|
Basic knowledge of probability theory and multivariate statistics.
|
|
Assessment methods and criteria
|
Oral exam, Seminar Work
Credit: Presentation of a project covering topics of statistical learning. Exam: oral.
|
|
Recommended literature
|
-
B. Efron, R. Hastie. (2017). Computer age statistical inference. Cambridge University Press, Cambridge.
-
Everitt, B., Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer, Heidelberg.
-
I. Goodfellow, Y. Bengio, A. Courville. (2016). Deep Learning. MIT Press, Boston.
-
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An introduction to statistical learning. An introduction to statistical learning.
-
T. Hastie, R. Tibshirani, J. Friedman. (2016). The elements of statistical learning. Springer, New York.
|