105.712 AKFVM AKSTA AKINF Machine Learning Methods and Data Analytics in Finance and Insurance
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VU, 2.0h, 3.0EC, to be held in blocked form


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Learning outcomes

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.

Subject of course

  • 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

Teaching methods

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

Mode of examination


Additional information

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).


  • Shevchenko, Pavel


Course dates

Wed09:00 - 09:0103.06.2020 CANCELLEDcourse was planned from 03.06.2020 to 03.07.2020
Course is held blocked

Examination modalities

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

Course registration

Begin End Deregistration end
31.01.2020 00:00 17.05.2020 23:59 07.06.2020 23:59



No lecture notes are available.