Computer vision today is driven by deep networks, but the way we design, train, and reason about them is deeply rooted in classical vision and signal processing. To truly do research and build creative new methods, you need both: learning and understanding pixels. MCV is roughly 70% deep learning for vision and 30% the classical foundations that enable it.
We go from the very basic math all the way to state-of-the-art practice. The goal is to get to the bottom of things, develop real intuition, and encourage creative new thinking. Five homework sets each have three tracks: theory, from-scratch implementation, and applied. The course has a research orientation and concludes with a final research project.
Due to the current security situation, all meetings will take place virtually until further notice.
Join Zoom MeetingSundays, Spring 2026. Lecture 13:30–16:30, Tutorial 16:30–17:30.
Cooper Building (Data Science), Room 216.
3.5 credits. 5 homework sets (theory + from-scratch + applied) and a final research project.
00940412 & 01040166 & 00960411
Teaching Assistant
Teaching Assistant