Harvard University
Prof. Finale Doshi Velez & Prof. David Parkes
Time: TTh 10:30-11:45am
Location: Zoom
CS 181 provides a broad and rigorous introduction to machine learning, probabilistic reasoning and decision making in uncertain environments. We will discuss the motivations behind common machine learning algorithms, and the properties that determine whether or not they will work well for a particular task. You will derive the mathematical underpinnings for many common methods, as well as apply machine learning to challenges with real data. In doing so, our goal is that you gain a strong conceptual understanding of machine learning methods that can empower you to pursue future theoretical and practical directions. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, reinforcement learning.
The material is aimed at an advanced undergraduate level. Students should be comfortable with writing non-trivial programs (e.g., CS 51, CS 61, or equivalent). All staff-provided code will be in Python. Students should also have a background in probability theory (e.g., STAT 110 or equivalent), and familiarity with calculus and linear algebra (e.g., AM 22a or Math 21ab, or equivalent).