Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition

The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical analysts (quants, in other words).Some of the work presented in this book is new, particularly the sections on inverse reinforcement learning.This will be an excellent resource for a graduate course. Those students who do not have the math background will likely be motivated to get it. There are well-designed Python notebooks that present examples of the analysis. Students with the ability to work with the concepts presented in this book would be welcome in any serious quant shop.

An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change is going to come from applications of this book's concepts. The authors also do a great job of demonstrating that these "black boxes" are actually not mysterious and overly complicated but rather fairly intuitive and implementable. If anyone has ever seen the movie "AlphaGO" and was wondering how that type of paradigm shift would apply to finance, the next step is to buy this book.

Brand new but I anticipate this will becomeTHE textbook on the subject that many instructors will use to teach around the world.The book nicely builds up throughout the chapters.I find it great to include multiple choice questions, exercises and an extra instructor booklet available to assist in the classroom.And what a great idea to share Python code to make it all that more practical!I find the insights on inverse reinforcement learning particularly interesting.

This product arrived in excellent condition.

As a first edition it is a bit less polished than I would like, but the topic coverage can't be found anywhere else. Top 1 of 1 books covering deep learning in finance. Looking forward to picking up future iterations of this book.

This book serves as high-level overview of a bunch of important statistical, ML and econometric concepts. So if you're already familiar with statistical learning and neural nets, it's a great guidebook and it introduces some relevant finance application.

Great bookWhere can I find the source codes?

This book represents a very comprehensive guide to Machine Learning techniques in Finance and serves remarkably well both the students of quantitative and computational finance, as well as a large cross-section of industry’s practitioners. As someone who worked in this field for several decades, and wrote a book on this topic, I understand very well what it takes to put together a comprehensive guide on such a subject. Very rare are the books on this topic that address properly at the same time both the theoretical aspects of the problem at hand, as well as exemplifying these concepts with meaningful practical examples.A book on application of ML to Finance is not necessary about providing tons of Py codes, and this one serves the reader very well. But it should be about setting up a strong foundation for the theories and concepts that underlie the ML machinery, as well as exemplifying these concepts with model reasoning that is applicable to real world problems. And the reader is served again extremely well from this perspective.In my opinion this book introduces several novelties in a very crowded and sometimes over published field:- A very elegant and well documented exposition on financial times series modelling, especially regarding the use of RNNs and Kalman filtering techniques;- A mathematically sound and well exemplified section on the use of Reinforcement Learning, specifically promoting the use of Inverse Reinforcement Learning and Imitation Learning as modern tools for Optimal Control;- Blending the ever-powerful set of Bayesian thinking into the world of financial ML, by developing an intuition for the role of functional regularization in the classical statistical setting.I read this book and followed its Py code examples with great pleasure, and I strongly recommend it to anyone that is interested in applying these modern concepts, while having a solid understanding of the Mathematics that supports it. Enjoy it!