This course aims to provide a high-level overview of machine learning and its applications in the business world. The goal of the course is to offer a broad, relatively non-technical introduction to key ideas from machine learning without digging too far into the mathematical minutiae. We hope the course will be accessible to students with an understanding of basic statistical concepts and mathematics but no deeper training. While one cannot do machine learning without coding, the course is not about coding and will not require any coding expertise. Overall, we hope students will leave the course able to have informed conversations about machine learning, understanding key conceptual ideas, and having a better idea of realistic opportunities for the use of machine learning in business as well as understanding potential challenges.
We will cover the two basic learning paradigms: (i) supervised learning and (ii) unsupervised learning. We will illustrate the paradigms through applications. Through the applications, we will highlight important concepts and practical considerations. After covering basic learning methods, we will explore additional topics such as deep neural networks, reinforcement learning, large language models, causal inference, and AI agents.
