Machine Learning for Business Students: An Experiential Learning Approach

Abstract

This paper reports on the design and teaching of a course on Machine Learning (ML) for business students, focusing on the interaction between content and teaching approach. We demonstrate that the nature of ML technology is amenable to experiential learning and is well suited for business students as users of the technology. The community-based tools, documentation and datasets enable non-programmers to use and adapt open, public-domain ML examples. Students learn how to select algorithms for specific data and tasks, experiment with hyper-parameters and neural-network structures, evaluate results and interpret their business implications. We conclude that business students, at least in our school, often have good abstract understanding of computing and may be ready for deeper learning of digital technology. The general-purpose nature of ML makes it suitable for real-world business problems, making it business-relevant technology that should be introduced into business schools.