105.712 AKFVM AKSTA AKINF Machine Learning Methods and Data Analytics in Finance and Insurance
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2020S, VU, 2.0h, 3.0EC, wird geblockt abgehalten
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LVA-Bewertung

Merkmale

  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: VU Vorlesung mit Übung

Lernergebnisse

Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage...

After successful completion of the course, students are able to apply computing and statistical tools to undertake quantitative modelling activities required from risk modellers and quantitative analysts in modern financial institutions and insurance companies.

This course focuses on machine learning and data analytics methods in applications for finance and insurance. The topics include regression models (including tree methods, boosting, bagging and random forest), neural networks, clustering, Bayesian methods, modelling dependence via copula, and Markov chain Monte Carlo methods. The course aims to develop a core mathematical and statistical understanding of the methods and their applications to problems in the field. The methods will be applied using the R language.

Inhalt der Lehrveranstaltung

  • Clustering methods
  • Regression methods (GLM, GAM)
  • Neural networks
  • Regression trees methods (including boosting, bagging, random forest)
  • Bayesian methods
  • Markov chain Monte Carlo methods
  • Modelling dependence via copula

Methoden

Lecture, with black board and projector.  Theory as well as applications.  Feedback to 1st homework during the course.

Prüfungsmodus

Prüfungsimmanent

Weitere Informationen

Anwesenheitspflicht! / Compulsory attendance!

The speaker will use the statistical software R with the editors RStudio and Jupyter Notebook during the lectures. It is recommended for participants to bring along their own laptop with the most recent version of R installed and either a good R programmer editor or R IDE (e.g., the open source edition of RStudio).

Vortragende

Institut

LVA Termine

TagZeitDatumOrtBeschreibung
Mi.09:00 - 09:0103.06.2020 CANCELLEDcourse was planned from 03.06.2020 to 03.07.2020
LVA wird geblockt abgehalten

Leistungsnachweis

1st homework during the course and individual project / data set for each student (2nd homework) after the course.

LVA-Anmeldung

Von Bis Abmeldung bis
31.01.2020 00:00 17.05.2020 23:59 07.06.2020 23:59

Curricula

StudienkennzahlSemesterAnm.Bed.Info
860 GW Gebundene Wahlfächer - Technische Mathematik

Literatur

Es wird kein Skriptum zur Lehrveranstaltung angeboten.

Sprache

Englisch