# TU Wien:Advanced Methods for Regression and Classification VU (Filzmoser)

## Contents

## Daten[edit]

Lecturers | Peter Filzmoser |
---|---|

ECTS | 4,5 |

Department | Stochastik und Wirtschaftsmathematik |

When | winter semester |

Language | English |

Links | tiss:105707, Homepage |

Master Data Science | Pflichtmodul MLS/FD - Machine Learning and Statistics - Foundations |

Mattermost: Channel **"advanced-methods-for-regression-and-classification"** Team invite & account creation link Mattermost-Infos

## Inhalt[edit]

This lecture is basically the english (new?) version of "Klassifikation und Diskriminanzanalyse".

Theory of models and methods for regression and classification and how to use them in R.

- Linear Regression
- Model comparison and model selection
- Linear Methods: OLS (ordinary least squares), PCR, PLS, Shrinkage methods
- Linear Methods for classification: Linear and Quadratic Discriminant Analysis (LDA, QDA)
- Non-linear Methods for regression: Basis expansions (splines), Generalized Additive Models (GAM)
- Tree-based methods for regression and classification, random forests
- Support Vector Machines (SVM) for regression and classification (not much theory here, more applications)

## Ablauf[edit]

Lectures in English. Presentation of Material from course notes + sketches on the blackboard. Material covers theoretic stuff, empirical methods, and realization / implementation in R (relevant packages, functions, code examples). Exercises to solved using R.

## Benötigte/Empfehlenswerte Vorkenntnisse[edit]

Some knowledge of regression is definitely nice to have, but regression is explored in-depth in all kinds of variants. In general: linear Algebra, matrix & vector calculus, basic probability theory for following the lecture notes. A certain level in R is definitely required (however, imho, R can be picked up quite fast, if you have sufficient programming skills in some language, say, python)

## Vortrag[edit]

Explanations to the lecture notes that are used as slides / presented with projector. Sometimes extra material, more in-depth explanations with sketches on the blackboard. As ususal, (imho) a good mix of first theory and models, than more hands-on stuff on how to use the methods in R, with some concrete data.

Attending the lectures is a good idea, as the methods for the exercises are usually discussed, as well as some of the R methods needed. In the exercise classes, students' solutions are shown and discussed, which can be really helpful to learn from these solutions and/or mistakes. Students can volunteer to show their solutions.

## Übungen[edit]

WS 2018: 11 excercises, to be programmed in / solved with R. Topics range from regression models to classification

## Prüfung, Benotung[edit]

noch offen

### Dauer der Zeugnisausstellung[edit]

noch offen

## Zeitaufwand[edit]

A few hours for the (almost) weekly exercise assignments. The amount will depend on your previous knowledge of the theoretic material, usage of R and whether you attend the lecture or not (which is imho very helpful!)

## Unterlagen[edit]

Lecture notes in german and english available. Skriptum in Deutsch und Englisch wird zur Verfügung gestellt.

Recommended books:

The Elements of Statistical Learning (Hastie, Tibshirani, Friedman)

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)

## Tipps[edit]

noch offen

## Verbesserungsvorschläge / Kritik[edit]

noch offen