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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In this chapter, you got a brief overview of modern Bayesian machine learning and its applications in finance. We've only touched upon this as it is a very active field of research from which we can expect many breakthroughs in the near future. It will be exciting to observe its development and bring its applications into production.

Looking back at this chapter, we should feel confident in understanding the following:

  • The empirical derivation of Bayes formula

  • How and why the Markov Chain Monte Carlo works

  • How to use PyMC3 for Bayesian inference and probabilistic programming

  • How these methods get applied in stochastic volatility models

Notice how everything you have learned here transfers to bigger models as well, such as the deep neural networks that we've discussed throughout the entirety of the book. The sampling process is still a bit slow for very large models, but researchers are actively working on making it faster, and what you've learned is a great foundation for the future.