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.
Announcements
- If you have any questions about the waitlist process, make sure to take a look at the Waitlist FAQ.
- If you would like to capstone this course, please review this document for details about how to do so.
- Please familiarize yourself with the course details and policies detailed in the Course Missive.
- Fill out the Collaboration Policy Form. You will not be able to hand in assignments without doing so.
- Sign up for Piazza. Please use Piazza for all questions about homeworks and course material.
- Please submit anonymous feedback, comments, and concerns with this link.
- Here is the sample final and sample final solutions in preparation for the final exam!
- HERE IS THE FINAL EXAM as well as a tex file and figures so that you can LaTeX your solutions with a template!
- Final exam solutions here