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.

• 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

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

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).

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