An Introduction to Machine Learning and Quantitative Finance
English | 2021 | ISBN: 1786349361 | 263 Pages | PDF (True) | 35 MB
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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