CS 4476Β 

Computer Vision

(Spring 2024)

Course Description

This is an introductory undergraduate course to computer vision which strikes the balance of classic vision topics such as light and color, image filtering, feature detection, transforms, stereo vision etc., and modern vision topics such as deep learning based visual recognition, generative models, transformers and diffusion models. The focus of the course is to develop intuitions and mathematics of the methods in the lectures, and then to learn about the difference between theory and practice in the projects.


Schedule is tentative and subject to change.

Links to recent/next lecture slides PDF and lecture recordings are shared through Canvas.

Textbook is not required; however, for those seeking an accessible textbook to accompany the course, you can check out the resources here. (Szeliski 2nd ed)

We use Ed Dicussion for more direct online communication.

Assignments, Exams and Grading

Final Grades: A: 90%+, B: 80%+, C: 70%+, D: 60%+, F<60%.

Teaching Assistants

TA Office Hours:Β Β 


TA Contacts:

Useful Links:



Course materials are built on top of contents from great colleagues including but not limited to: Judy Hoffman, James Hays, Svetlana Lazbnik, Fei-Fei Li, Kristen Grauman, Devi Parikh, Derek Hoiem, Bill Freeman, Alexei Effros and many more.