The purpose of this training is to introduce participants to neural networks for actuarial pricing with a strong emphasis on the practical implementation in a R library.
The training proceed step by step, recalling the fundamental statistical concepts at the heart of tree-based methods. Their relative merits are illustrated by means of several case studies with insurance data.
The aim of this course is to introduce the local and global methods analyzing relations between output and input of complex ML algorithms.
The aim of this session is to cover unsupervised learning techniques for visualizing and analyzing a dataset. This course focuses on actuarial applications of clustering methods.
This training gives you a general knowledge of insurance analytics. You will be able to select the appropriate approach for your own data, run the R code and interpret the results.
