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

Very few books have this much detailed explanation. And truly speaking this is raw EXPLANATION, not exaggeration, unlike other ones. Bayesian modeling, Gaussian process, RL - all are explained superbly. A must-have for quants and data scientists.

The content is great but the book arrived damaged. The hard cover of the book is fold and in bad condition.

Now that I've integrated bayesian modeling in my work (as in my presentation "The Right Kind of Volatility" at QuantMinds 2020), I can appreciated more how this book takes its time guiding the reader through the steps of choosing and judging models. The chapters on Reinforcement Learning are more advanced, but worth the time spent learning (I was inspired by the QLBS approach to Black and Scholes). Examples and code make it an outstanding book for those interested in learning more about financial modeling in the 20s.

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!

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.

I just started to read the book and I have found it to be very informative for people with interests and background in quantitative finance. Machine Learning, Artificial Intelligence and specially Reinforcement Learning is currently a focus point of research as there has been interesting breakthroughs, e.g. DeepMind's AlphaGo. Financial industry is also benefiting from the machine learning advancements, specially when non-traditional alternative data are available, e.g. sentiment-based trading or natural language processing.The book authors have extensive experience and background in quantitative finance. The book aims to presents the machine learning subject for quantitative finance professionals and graduate students in quantitative disciplines, e.g. Mathematics, Physics, Statistics. The book is divided to three parts: Machine Learning with Cross-Sectional Data, Sequential Learning, and Sequential Data with Decision-Making. Each part encompasses relevant topics presented in a few chapters where each chapters is accompanied by corresponding reference aiding interested readers to dive into the chapter's material. The book is also accompanied with a collection of Python codes to further facilitate the learning process.For readers with knowledge of option pricing, optimal hedging the reinforcement learning part of the book provides the dynamic programming approach toward relevant classical option pricing problems through reinforcement learning closely resembling the celebrated Black-Scholes-Merton model.Overall, the book is valuable resource for Quants to become acquainted with the emerging Machine Learning Applications in Finance. The book should be helpful to the whole Machine Learning, and Artificial Intelligence community, and in particular to quants community in financial industry.