Universität Passau
35622 Vorlesung: Computational Statistics - Statistical Learning in R - Details
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Allgemeine Informationen

Untertitel
Veranstaltungsnummer 35622
Semester SoSe 21
Aktuelle Anzahl der Teilnehmenden 123
erwartete Teilnehmendenanzahl 20
Heimat-Einrichtung Lehreinheit für Computergestützte Statistik und Mathematik
beteiligte Einrichtungen Graduiertenzentrum, Lehrstuhl für Statistik und Data Analytics
Veranstaltungstyp Vorlesung in der Kategorie Lehre (mit Prüfung)
Erster Termin Di., 13.04.2021 12:00 - 14:00 Uhr, Ort: (Online-Veranstaltung (Zoom))
Art/Form
Voraussetzungen
Knowledge of statistics and regression methods on master level and basic knowledge of R (e.g. via 'Computational Statistics – Regression in R').
Lernorganisation
Guided computer tutorials; students are expected to deepen their knowledge by completing self-contained exercises in R.
Leistungsnachweis
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)
Sonstiges
Course is taught in english.
ECTS-Punkte
3

Veranstaltungsort / Veranstaltungszeiten

(Online-Veranstaltung (Zoom)) Dienstag: 12:00 - 14:00, wöchentlich (13x)

Studienbereiche

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Kommentar/Beschreibung

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.