Machine Learning in Finance: From Theory to Practice Paperback – 2 July 2021

This book covers a vast number of highly relevant machine learnings topics in an accessible manner (even for non-ML experts) and illustrates their application to finance and other fields with numerous examples in the book and additional exercises or coding applications (Python) for the interested reader.The exposition throughout the books is clear and consistent with plenty of colourful illustrations to reinforce the concepts. The level of prerequisite knowledge is kept to a minimum where possible -- although having an undergraduate degree in maths, physics, statistics, or a related quantitative field will certainly help in studying this book.Being a finance professional with a quantitative background, for me this book provides a deep insight into how ML can be used across various hot topics in quant finance (e.g. algorithmic trading), but also other non-financial disciplines.Impressive work by the authors who showcase their extensive knowledge in the field -- a must buy!

I have a decent understanding of Machine Learning, and wanted to know more about its applications in Finance. It has been a very useful book, as it is rare to find books covering applications of ML in Finance. The best part about this book is that, it also covers various foundational disciplines like Maths & Statistics wherever I felt there was a need for it.I like the fact that it comes with exercises at the end of chapters, and quite a lot of code samples that can be readily executed to understand the concepts. Colour images are a big bonus too.There was a minor issue, 4 or 5 of the colour images, have black text on very dark backgrounds, hence making them unreadable, luckily many of these can be read by executing the code samples, so, it was not a big issue for me. I would have deducted about 0.25 stars for the image issue, but I can't do that, and it is otherwise an excellent addition to my learning.I believe that it will also be equally good for Finance professionals who want to know about Machine Learning, while I belong to the ML group wanting to learn it's applications in Finance.

One slight problem that this book didn't work with my Kindle Paperwhite.

It was a real privilege to be asked to review this book from a delivery and wider team perspective than straight quant finance by my industry peers.As an eighteen year old Physics undergraduate I was taught: you cannot build a house on weak foundations consequently I was taught the mathematical skills I needed and a physicist intuition to be a good Physicist.This textbook will do the same for you in Machine Learning giving you the foundation and intuitions through1. Great academic references2. Clear objectives and conclusions for each chapter3. Many examples for students with complimentary answer book for teachers4. Mathematics presented in an approachable way, which is important to me.Part 1: Machine Learning with Cross sectional dataI like the approach of the book as aim to foundations are laid to provide a solid mathematical framework and intuition to deliver Machine Learning solutions. What Matthew refers to as “mathematical machinery”.Part 2: Sequential LearningLooks at the linkages to finance, econometrics and Machine Learning it aims to address the question “Why do you need to deploy machine learning?” and presents the reasoning behind architectural choices for the deployed – removing the guesswork.Part 3: Sequential Data and Decision MakingLooks at reinforcement learning (RL) and inverse reinforcement learning (IRL) I particularly liked the use case of the “invisible hand” and IRL. You will have to check the book for it.The book finishes with parallels to the Grand Unification theory which for me as a physicist gives me that warm feeling I am in a good space (if you pardon the joke).The book is a great place to build your understanding of the subject with the hype stripped away. From someone who must turn these ideas to business benefits in an organisation I would gladly recommend this book.Satinder

I thoroughly enjoyed reading this book. The authors cover, in great detail, many of the key ML concepts and architectural implementations that are both relevant academically, but also have great practical utility in a financial setting (I speak as a Data Scientist for a large commodity trading outfit). What is particularly helpful, that that the authors solicit and reference a number of successful research papers throughout the book, unlike, for example, many of De Prado's publications whose goal largely appears to be that of proving why everyone else is wrong and simply reference his own work as proof.

The link with the code mentioned in the book is not valid. I tried to contact the site admin she recommended me to find the authors and contact them directly, I also tried to do it via Linkedin, negative outcome. It is an academic source, if I buy the book it has work.Thanks

Traditionally finance industry uses mathematical approaches on so-called from "quantitative finance" perspective. Dixon-Halperin-Bilokon's refreshing book does not only capture specialised usage of machine learning in finance but it also serves as a machine learning reference book. They treat chapters in great substance with carefully covering basic concepts in a non-superficial manner.