Welcome to cs1420!
How can artificial systems learn from examples, and discover information buried in massive datasets? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include empirical risk minimization, probably approximately correct learning, maximum likelihood parameter estimation, kernel methods, neural networks, the expectation maximization algorithm, and principal component analysis.
- Due to a large number of requests, we're asking anyone who is unable to register through C@B for CS 1420 to join the waitlist here, in order to allocate any available spots as fairly as we can. There is also a waitlist FAQ available here
- The anonymous feedback form for the course can be found here. The form does not record your email, but you will be required to log into a brown account to verify that you are a student.