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

Exercises


To get more comfortable with generative models, try your hand at these exercises:

  1. Create an SGAN in order to train an MNIST image classifier. How few images can you use to achieve over 90% classification accuracy?

  2. Using LSTMs, you can build an autoencoder for stock price movements. Using a dataset such as the DJIA stock prices, build an autoencoder that encodes stock movements. Then visualize what happens to the outputs as you move through the latent space. You can find the dataset here: https://www.kaggle.com/szrlee/stock-time-series-20050101-to-20171231.