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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

3. Utilizing Computer Vision

4. Understanding Time Series

5. Parsing Textual Data with Natural Language Processing

6. Using Generative Models

7. Reinforcement Learning for Financial Markets

8. Privacy, Debugging, and Launching Your Products

9. Fighting Bias

10. Bayesian Inference and Probabilistic Programming

Index

Legal perspectives

There are two doctrines in anti-discrimination law: disparate treatment, and disparate impact. Let's take a minute to look at each of these:

Disparate treatment: This is one kind of unlawful discrimination. Intentionally discriminating against ZIP codes with the hope of discriminating against race is not legal. Disparate treatment problems have less to do with the algorithm and more to do with the organization running it.
Disparate impact: This can be a problem if an algorithm is deployed that has a different impact on different groups, even without the organization knowing about it. Let's walk through a lending scenario in which disparate impact could be a problem. Firstly, the plaintiff must establish that there is a disparate impact. Assessing if there's a disparate impact is usually done with the four-fifths rule, which says that if the selection rate of a group is less than 80% of the group, then it is regarded as evidence of adverse impact. If a lender has 150 loan...

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

See other products by Jannes Klaas

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