This course offers a broad introduction to key concepts in machine learning, with a focus on applications in the business world. We will cover the three core learning paradigms: (i) supervised learning, (ii) unsupervised learning, and (iii) reinforcement learning. Each paradigm will be illustrated through simple examples designed to highlight essential ideas and practical considerations. In addition to the basic learning methods, we will explore topics - such as causal inference, transfer learning and fine-tuning deep neural networks, and AI agents - as time permits. The goal is for students to leave the course with a solid grasp of core machine learning concepts, the ability to engage in informed discussions, and a clearer sense of both the opportunities and limitations of applying machine learning in business contexts.
The course is introductory and will not dig into the mathematical minutiae of machine learning. It is meant to be accessible to students with an understanding of basic statistical concepts and mathematics. Importantly, the course is not about coding and will provide no formal instruction in programming. However, coursework will involve implementing machine learning methods, so students without a coding background will need to be self-directed and make use of external resources to complete assignments. Students are strongly encouraged to use AI tools to support coding, interpretation, and learning throughout the course.
