When professor John Hull saw the limited resources available for business professionals on machine learning topics, he set out to write and publish his own. In Machine Learning in Business: An Introduction to the World of Data Science (2019), Hull clearly explains the fundamental principles of machine learning to the business professional, while highlighting relevant applications.

“Most books on machine learning are written by computer scientists for computer science students. My book is aimed at business professionals. It introduces the algorithms and underlying thinking so that they can work productively with data scientists,” says Hull, who is a professor in the finance area and the Maple Financial Group chair in derivatives and risk management at the Rotman School, as well as the academic director of the FinHub, the School’s financial innovation lab.

Hull uses data sets on country risk, housing prices and loan defaults to illustrate the algorithms. Reinforcement learning — which, in addition to its business applications, has been successfully used to beat the best human chess and Go players — is compellingly illustrated with a much simpler game, Nim. One of the attractive features of his book are the accompanying Excel and Python files, available for download on his website, for all the applications described.

The last chapter of the new book deals with the issues machine learning has created for society. There are growing concerns around data privacy, biases in algorithms, ethics, and potential job losses. Still, Hull is optimistic and quick to quell any fears that machine learning will displace talented professionals.

“With every past industrial revolution, some job functions have become obsolete, but many new roles have been created. We should be focused on the interesting applications and new jobs that will emerge,” Hull says.

His advice to students and practitioners: if you’re after a long and interesting career in the finance industry, you need to know about machine learning.