Machine learning and data analytics is an emerging field that is beginning to have a strong influence on the field of actuarial science practice. The onset of big data applications in insurance has driven the profession to explore new ways to understand data and modelling. Unlike in Google and Facebook type technology applications where huge data bases of labelled data are available, in the insurance context we are often considering unsupervised learning methods. This course will address core methodology to tackle unsupervised problems of relevance to insurance applications.

• Introduction to unsupervised ML with context of insurance
• Brief statements of axiomatisation of clustering and impossibility theorem results for clustering
• Preparing data for clustering
• K-means clustering, K-centroids
  (with examples in cyber risk and insurance)
• Information theoretic interpretations and Bregmann bias
• Hard vs. soft assignment methods and probabilistic clustering
• Expectation-maximization methods for clustering.
• Frequentist and Bayesian-EM variants
• Applications of EM algorithm and variants.
• (claims reserving examples)
• Feature maps and kernel maps.
• Non-linear clustering via kernel k-means
  (telematics data)
• Families of kernels
• Kernel target alignments and hyper-parameter tuning
• Feature extraction methods
• Probabilistic PCA and robust PPCA factor models
• Mortality modelling examples
• Un-supervised multi-kernel learning
• Classification trees and random forests
  (home insurance - aggregators)
• Ada boost and bagging

The speakers will use the statistical software R with the editor RStudio during the lectures. Participants can (but do not have 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).

Attendance of all parts of the course is required and will be checked.
Additional requirements (e.g., excercise classes, homework, oral exam) will be announced as soon as possible.

The course is announced as "VU" (Vorlesung mit integrierter Übung) - this means it is a lecture with a practical part (exercises).  Parcipation is necessary and there will be an additional homework - every registered student will be graded.

Advanced students of mathematics (Financial and Actuarial Mathematics, Statistics and Mathematics in Economics, Technical Mathematics) will be accepted preferable. But the course might also be interesting for students of informatics (with interest in maths&statistics).

In case the lecturers provide electronic material (e.g., slides, R codes) this will be distributed to the students.  This applies also to material from additional speakers, which are not mentioned at the course.