Statistical Foundations of Actuarial Learning and its Applications (Springer Actuarial) (1st ed. 2023. 2022. xii, 605 S. XII, 605 p. 1 illus. 235 mm)

- 1. Introduction. - 2. Exponential Dispersion Family. - 3. Estimation Theory. - 4. Predictive Modeling and Forecast Evaluation. - 5. Generalized Linear Models. - 6. Bayesian Methods, Regularization and Expectation-Maximization. - 7. Deep Learning. - 8. Recurrent Neural Networks. - 9. Convolutional Neural Networks. - 10. Natural Language Processing. - 11. Selected Topics in Deep Learning. - 12. Appendix A: Technical Results on Networks. - 13. Appendix B: Data and Examples.