Computational Statistics - Statistical Learning in R
35622
Vorlesung:
Computational Statistics - Statistical Learning in R
(SoSe 24)
Course times
Di. 12:00 - 14:00 (wöchentlich)
Course venue
nicht angegeben
Start date
Dienstag, 16.04.2024 12:00 - 14:00 Uhr
Teaching contact hours per week
2
Description
Statistical Learning sums up methods from computational statistics that are designed to deal with high dimensional, complex data sets. Various topics that facilitate modeling of and gaining a deeper insight into high dimensional, complex data sets are introduced. Basic subervised and unsupervised statistical learning techniques are presented, discussed, and applied in class (For example hierarchical clustering, linear and nonlinear classification and regression techniques, incorporating lasso, random forests, bagging, boosting, etc.). Meta-parameter selection, model evaluation, and specification choice in practical settings are also covered in the course.
Pre-requisites
Knowledge of statistics and regression methods on master level and basic knowledge of R (e.g. via 'Computational Statistics – Regression in R').
Mode of study
Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained exercises in R.
Assessments
Final exam (60 minutes); R-skills are certified via a certificate when the final exam is passed.
Indicative reading list
- Kuhn, M. & Johnson, K. (2013), Applied Predictive Modeling, Springer.
- Hastie, T., Tibshirani, R. & Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2Ed., Springer.
- Efron, B., Hastie, T. (2016), Computer Age Statistical Inference, Cambridge University Press.
- Torgo, L. (2017), Data Mining with R: Learning with Case Studies, 2Ed., CRC Press.
- James, G., Witten, D., Hastie, T & Tibshirani, R. (2015), An Introduction to Statistical Learning: with Applications in R, Springer.
Additional information
Course is taught in english.
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