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.
- Final exam solutions have been released! The password can be found on Piazza, as always.
- May 7, 2020: The final exam has been released and is due May 8th at 11:59pm ET. A LaTeX version of the final is here. Good luck!
- A sample final as well as its solutions have been posted. A latex version of the final is here. The full exam instructions are available in the documents.
- A sample midterm as well as its solutions are posted to help you review the material so far in the course. Please check Piazza for the latest updates regarding changes to class structure and schedule. As of 8pm 3/14/20, the lecture and assignment schedule posted on this website reflect how we anticipate the schedule to look for the rest of the semester.
- 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.