Lecture: 35622 Computational Statistics - Statistical Learning in R - Details

Lecture: 35622 Computational Statistics - Statistical Learning in R - Details

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General information

Course name Lecture: 35622 Computational Statistics - Statistical Learning in R
Subtitle
Course number 35622
Semester SoSe 22
Current number of participants 122
expected number of participants 20
Home institute Lehreinheit für Computergestützte Statistik und Mathematik
participating institutes Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Courses type Lecture in category Lehre (mit Prüfung)
First date Tuesday, 26.04.2022 12:00 - 14:00 Uhr
Type/Form
Pre-requisites
Knowledge of statistics and regression methods on master level and basic knowledge of R (e.g. via 'Computational Statistics – Regression in R').
Learning organisation
Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained exercises in R.
Performance record
Final exam (60 minutes); R-skills are certified via a certificate when the final exam is passed.
SWS
2
Literatur
  • 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.
Qualifikationsziele
The course aims at providing participants with a basic understanding of some of the core concepts and building blocks of Statistical Learning. An additional goal of the course is to equip students with a variety of techniques to analyze high dimensional, complex data sets by means of the freely available statistical software R and to judge the appropriateness of the respective procedures for different data constellations.
Workload
2 SWS (30 h attendance, 45-60 h self-study)
Miscellanea
Course is taught in english.
ECTS points
3

Rooms and times

No room preference
Tuesday: 12:00 - 14:00, weekly

Fields of study

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Comment/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.