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
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Chapter 10. Bayesian Inference and Probabilistic Programming

Mathematics is a big space of which humans so far have only charted a small amount. We know of countless areas in mathematics that we would like to visit, but that are not tractable computationally.

A prime reason Newtonian physics, as well as much of quantitative finance, is built around elegant but oversimplified models is that these models are easy to compute. For centuries, mathematicians have mapped small paths in the mathematical universe that they could travel down with a pen and paper. However, this all changed with the advent of modern high-performance computing. It unlocked the ability for us to explore wider spaces of mathematics and thus gain more accurate models.

In the final chapter of this book, you'll learn about the following:

  • The empirical derivation of the Bayes formula

  • How and why the Markov Chain Monte Carlo works

  • How to use PyMC3 for Bayesian inference and probabilistic programming

  • How various methods get applied...