Effective Statistical Learning Methods for Actuaries III

Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance.The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics.Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting.This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.

Preface. - Feed-forward Neural Networks. - Byesian Neural Networks and GLM. - Deep Neural Networks.- Dimension-Reduction with Forward Neural Nets Applied to Mortality. - Self-organizing Maps and k-means clusterin in non Life Insurance. - Ensemble of Neural Networks.- Gradient Boosting with Neural Networks. - Time Series Modelling with Neural Networks.- References.