An Introduction to Machine Learning in Quantitative Finance (Advanced Textbooks in Mathematics) (Paperback)

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An Introduction to Machine Learning in Quantitative Finance (Advanced Textbooks in Mathematics) (Paperback)

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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.
  • 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

Product Details ISBN: 9781786349644
ISBN-10: 1786349647
Publisher: Wspc (Europe)
Publication Date: March 26th, 2021
Pages: 264
Language: English
Series: Advanced Textbooks in Mathematics