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CS 181: Machine Learning (2020)

Finale Doshi-Velez, Harvard University

Lectures: Mon/Wed 9-10:15 am, Maxwell Dworkin G115


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  • HW6 has been released and is due Monday, April 27 7:59 am ET.


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.