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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

Heuristic, feature-based, and E2E models

Before we dive into developing models to detect fraud, let's take a second to pause and ponder over the different kinds of models we could build.

A heuristic-based model is a simple "rule of thumb" developed purely by humans. Usually, the heuristic model stems from having an expert knowledge of the problem.
A feature-based model relies heavily on humans modifying the data to create new and meaningful features, which are then fed into a (simple) machine learning algorithm. This approach mixes expert knowledge with learning from data.
An E2E model learns purely from raw data. No human expertise is used, and the model learns everything directly from observations.

In our case, a heuristic-based model could be created to mark all transactions with the TRANSFER transaction type and an amount over $200,000 as fraudulent. Heuristic-based models have the advantage that they are both fast to develop and easy to implement; however, this comes with a pay-off, their...

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Machine Learning for Finance

Published in: May 2019

Publisher: Packt

ISBN-13: 9781789136364

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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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