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• We know that finding a new normal is hard, and many of you may not be in ideal study situations. If there are ways we can adapt the way we do lectures, sections, office hours, homeworks, etc. to help, please let us know, and we will try our best!
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• HW6 has been released and is due Monday, April 27 7:59 am ET.

About

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.

Full course description with policies.