Resources

Office Hours

Prerequisites

This course will be self-contained; students do not need to have computer vision background. However, the following are required:

Data structures
Working knowledge of Python
Knowledge of how to use git and GitHub (clone, pull, commit, push, etc)
Linear algebra
Vector calculus

Please email the instructor if you are unsure of whether you can take the course.

Textbook

Computer Vision: Algorithms and Applications textbook cover

This course will have readings from Computer Vision: Algorithms and Applications, by Richard Szeliski. You can download a free copy at its website.

Canvas: https://canvas.cornell.edu/courses/72649
Ed discussion: https://edstem.org/us/courses/74398
Gradescope: TBD
Zoom: TBD

Additional Materials

Here are some additional materials that may be of use in learning about the theory and practice of computer vision.

Awesome Computer Vision: https://github.com/jbhuang0604/awesome-computer-vision
Stanford Course on CNNs: https://cs231n.stanford.edu/