Finale Doshi-Velez, David Alvarez, and Melis Stephanie Gil
COMPSCI 1810 | Spring 2025 | Course Listing
Tuesday & Thursday, 9:45 AM – 11:00 AM
Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. 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, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.
Recommended Prep: Computer Science 51 or 61, Statistics 110, Applied Math 22a or Math 21ab (or equivalent).