This course aims to offer a broad, relatively non-technical introduction to key ideas from machine learning without digging too far into the mathematical minutiae. I intend the course to be accessible to students with an understanding of basic statistical concepts and mathematics but no deeper training beyond standard MBA-level statistics. Overall, my goal is that students leave the course able to have informed conversations about machine learning, understanding key conceptual ideas and practical challenges, and having a better idea of realistic opportunities for the use of machine learning in business.
The course will start by covering two basic learning paradigms: (i) supervised learning and (ii) unsupervised learning. In class, I will illustrate the paradigms through small-scale applications styled to match business problems. These applications will provide a vehicle for discussing important concepts and practical considerations. After covering the basic learning methods, we will explore additional topics such as deep neural networks, causal inference, policy learning, reinforcement learning, and generative AI models.
