An introduction to machine learning in quantitative finance: advanced textbooks in mathematics
Material type:
TextPublication details: New Jersey World Scientific 2021 Description: xxiv, 238 pISBN: 9781786349644Subject(s): Machine learning | Finance--Mathematical modelsDDC classification: 332.0285631 Summary: 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!
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Book
|
Indian Institute of Management LRC General Stacks | Finance & Accounting | 332.0285631 NI (Browse shelf(Opens below)) | 1 | Available | 003772 |
Contents:
Preface
About the Authors
Acknowledgments
Disclaimer
Listings
Overview of Machine Learning and Financial Applications
Supervised Learning
Linear Regression and Regularization
Tree-based Models
Neural Networks
Cluster Analysis
Principal Component Analysis
Reinforcement Learning
Case Study in Finance: Home Credit Default Risk
Bibliography
Index
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!
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