Machine Learning in Finance : From Theory to Practice /

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothe...

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Bibliographic Details
Main Authors:	Dixon, Matthew F. (Author), Halperin, Igor (Author), Bilokon, Paul (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2020.
Edition:	1st ed. 2020.
Subjects:	Statistics . Applied mathematics. Engineering mathematics. Statistics for Business, Management, Economics, Finance, Insurance. Applications of Mathematics. Statistics, general. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1. Introduction
Chapter 2. Probabilistic Modeling
Chapter 3. Bayesian Regression and Gaussian Processes
Chapter 4. Feed Forward Neural Networks
Chapter 5. Interpretability
Chapter 6. Sequence Modeling
Chapter 7. Probabilistic Sequence Modeling
Chapter 8. Advanced Neural Networks
Chapter 9. Introduction to Reinforcement learning
Chapter 10. Applications of Reinforcement Learning
Chapter 11. Inverse Reinforcement Learning and Imitation Learning
Chapter 12. Frontiers of Machine Learning and Finance.