An Introduction to Machine Learning and Quantitative Finance: 0 (Advanced Textbooks In Mathematics)

Hao Ni,Guangxi Yu,Jinsong Zheng,Xin Dong

ISBN 10: 1786349647 / ISBN 13: 9781786349644

Editore: World Scientific Europe Ltd 2021-01-03, 2021

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Codice articolo 6666-GRD-9781786349644

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Titolo: An Introduction to Machine Learning and ...

Casa editrice: World Scientific Europe Ltd 2021-01-03

Data di pubblicazione: 2021

Legatura: Paperback

Condizione: New

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Riassunto:

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. Provide a GitHub repository https://github.com/deepintomlf/mlfbook.git that contains supplementary Python codes of all methods/examples. 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!