Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions

<p>This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible.</p> <p>Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. 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.</p> Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.<p></p><p></p> <p>This is the third of three volumes entitled <i>Effective Statistical Learning Methods for Actuaries</i>. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.</p><p></p><p><br></p><p><br></p><p></p><p></p>