Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Index

Catch – a quick guide to reinforcement learning


Catch is a straightforward arcade game that you might have played as a child. Fruits fall from the top of the screen, and the player has to catch them with a basket. For every fruit caught, the player scores a point. For every fruit lost, the player loses a point.

The goal here is to let the computer play Catch by itself. We will be using a simplified version in this example in order to make the task easier:

The "Catch" game that we will be creating

While playing Catch, the player decides between three possible actions. They can move the basket to the left, to the right, or make it stay put.

The basis for this decision is the current state of the game; in other words, the positions of the falling fruit and of the basket. Our goal is to create a model that, given the content of the game screen, chooses the action that leads to the highest score possible. This task can be seen as a simple classification problem. We could ask expert human players...