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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What is machine learning?

"Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed."

- Arthur Samuel, 1959

What do we mean by machine learning? Most computer programs today are handcrafted by humans. Software engineers carefully craft every rule that governs how software behaves and then translate it into computer code.

If you are reading this as an eBook, take a look at your screen right now. Everything that you see appears there because of some rule that a software engineer somewhere crafted. This approach has gotten us quite far, but that's not to say there are no limits to it. Sometimes, there might just be too many rules for humans to write. We might not be able to think of rules since they are too complex for even the smartest developers to come up with.

As a brief exercise, take a minute to come up with a list of rules that describe all dogs, but clearly distinguish dogs from all other animals. Fur? Well, cats have fur, too. What about a dog wearing a jacket? That is still a dog, just in a jacket. Researchers have spent years trying to craft these rules, but they've had very little success.

Humans don't seem to be able to perfectly tell why something is a dog, but they know a dog when they see a dog. As a species, we seem to detect specific, hard-to-describe patterns that, in aggregate, let us classify an animal as a dog. Machine learning attempts to do the same. Instead of handcrafting rules, we let a computer develop its own rules through pattern detection.

There are different ways this can work, and we're now going to look at three different types of learning: supervised, unsupervised, and reinforcement learning.