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.
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Free Chapter
Applying Machine Learning to Structured Data
Utilizing Computer Vision
Understanding Time Series
Parsing Textual Data with Natural Language Processing
Using Generative Models
Reinforcement Learning for Financial Markets
Privacy, Debugging, and Launching Your Products
Fighting Bias
Bayesian Inference and Probabilistic Programming
Index

An intuitive guide to Bayesian inference

Before starting, we need to import `numpy` and `matplotlib`, which we can do by running the following code:

```import numpy as np
import matplotlib.pyplot as plt% matplotlib inline```

This example is similar to the one given in the 2015 book, Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, written by Cameron Davidson-Pilon. However, in our case, this is adapted to a financial context and rewritten so that the mathematical concepts intuitively arise from the code.

Note

Note: You can view the example at the following link: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/.

Let's imagine that you have a security that can either pay \$1 or, alternatively, nothing. The payoff depends on a two-step process. With a 50% probability, the payoff is random, with a 50% chance of getting \$1 and a 50% chance of making nothing. The 50% chance of getting the dollar is the true payoff probability (TPP),...