TU Wien:Advanced Methods for Regression and Classification VU (Filzmoser): Unterschied zwischen den Versionen

Aus VoWi
Wechseln zu: Navigation, Suche
(fixe LVA-Daten (lva_fixer.py))
 
Zeile 1: Zeile 1:
 
 
== Daten ==
 
== Daten ==
 
{{LVA-Daten
 
{{LVA-Daten
Zeile 6: Zeile 5:
 
|abteilung=Stochastik und Wirtschaftsmathematik
 
|abteilung=Stochastik und Wirtschaftsmathematik
 
|homepage=http://cstat.tuwien.ac.at/filz/studklassdis1819.html
 
|homepage=http://cstat.tuwien.ac.at/filz/studklassdis1819.html
|tiss=105707
+
|id=105707
 
|wann=WS
 
|wann=WS
 
| sprache = en
 
| sprache = en
Zeile 76: Zeile 75:
 
== Verbesserungsvorschläge / Kritik ==
 
== Verbesserungsvorschläge / Kritik ==
 
noch offen
 
noch offen
 
  
 
<!-- Um den Artikel in die richtig einzuordnen bitte die Kategorien, die nicht auf diese LVA zutreffen, und die Kommentare löschen
 
<!-- Um den Artikel in die richtig einzuordnen bitte die Kategorien, die nicht auf diese LVA zutreffen, und die Kommentare löschen

Aktuelle Version vom 10. Juni 2019, 20:06 Uhr

Daten[Bearbeiten]

Vortragende Peter Filzmoser
ECTS 4,5
Abteilung Stochastik und Wirtschaftsmathematik
Wann Wintersemester
Sprache English
Links tiss:105707, Homepage
Zuordnungen
M. 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[Bearbeiten]

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[Bearbeiten]

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[Bearbeiten]

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[Bearbeiten]

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[Bearbeiten]

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

Prüfung, Benotung[Bearbeiten]

noch offen

Dauer der Zeugnisausstellung[Bearbeiten]

noch offen

Zeitaufwand[Bearbeiten]

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[Bearbeiten]

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[Bearbeiten]

noch offen

Verbesserungsvorschläge / Kritik[Bearbeiten]

noch offen


Materialien

Diese Seite hat noch keine Materialien, du kannst aber neue hinzufügen.