In today's world, we are increasingly exposed to the words 'machine learning', a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. In the past few years, machine learning has been introduced to the world of finance, reshaping the landscape of quantitative finance as we know it.Introduction to Machine Learning and Quantitative Finance aims to demystify machine learning by uncovering its underlying mathematics and showing how to apply machine learning algorithms to real-world financial data problems. Each chapter introduces problems around supervised learning algorithms, including linear models, tree-based models and neural networks, as well as unsupervised learning and reinforcement learning, followed by essential definitions and theorems in each case. Detailed guidance on the practical implementation of the algorithms is provided, and all codes are available on a GitHub repository. There are also exercises at the end of each chapter for readers to self-check their understanding.This interdisciplinary textbook provides a general framework of machine learning and provides a systematic treatment of modern machine learning methods, with ample examples to enhance the reader's understanding. Introduction to Machine Learning and Quantitative Finance provides not only theoretical knowledge of machine learning but also practical examples of financial applications. It will give readers hands-on experience in the field and enable them to apply the knowledge in this book to their own financial data problems.

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In today's world, we are increasingly exposed to the words "machine learning" (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.

An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authors

Provide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems.

Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning.

Summarize key contents of each section in the tables as a cheat sheet.

Include ample examples of financial applications.

Showcase how to tackle an exemplar ML project on financial data end-to-end.

Supplement Python codes of all the methods/examples in a GitHub repository.

Featured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!

The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https: //github.com/deepintomlf/mlfbook.git

"About this title" may belong to another edition of this title.

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