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.

2023S, VU, 2.0h, 3.0EC, wird geblockt abgehalten


  • Semesterwochenstunden: 2.0
  • ECTS: 3.0
  • Typ: VU Vorlesung mit Übung
  • Format der Abhaltung: Präsenz


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, 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)
  • Classification methods
  • Ridge and LASSO regularisation
  • Model selection/model assessment


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



Weitere Informationen

Anwesenheitspflicht! / Compulsory attendance!

The speaker will use the statistical software R with the editor RStudio 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 Personen


LVA Termine

Mo.10:00 - 14:0005.06.2023Zeichensaal 3 .
Di.11:00 - 14:0006.06.2023FH Hörsaal 3 - MATH .
Fr.13:00 - 16:0009.06.2023Zeichensaal 3 .
Mo.10:00 - 13:0019.06.2023Zeichensaal 3 .
Di.11:00 - 14:0020.06.2023FH Hörsaal 3 - MATH .
Fr.13:00 - 16:0023.06.2023Zeichensaal 3 .
Mo.10:00 - 13:0026.06.2023Zeichensaal 3 .
Di.11:00 - 14:0027.06.2023FH Hörsaal 3 - MATH .
LVA wird geblockt abgehalten


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


Von Bis Abmeldung bis
03.02.2023 00:00 11.06.2023 23:59 11.06.2023 23:59


860 GW Gebundene Wahlfächer - Technische Mathematik Gebundenes Wahlfach


Es wird kein Skriptum zur Lehrveranstaltung angeboten.