Course Materials

See the schedule page for links to most course materials.

GitHub classroom invite links and Gradescope links are on Moodle.

Computer Vision Textbooks

There are no textbooks that are a good enough fit to use as a course textbook. However, two do merit mention as secondary references:

• [RS] Computer Vision: Algorithms and Applications, 2nd ed., by Richard Szeliski (ISBN 3030343715). A PDF version of the book is available for free at http://szeliski.org/Book/.
• [TIF] Foundations of Computer Vision, by Torralba, Isola, and Freeman. A web version is available for here at https://visionbook.mit.edu/.

ML for Vision Quickstart Resources

• numpy quickstart
• The Little Book of Deep Learning
• Stanford’s CS231n: CNNs for Visual Recognition
  • python and numpy tutorial
  • Other well-written notes on ML fundamentals and CNNs: https://cs231n.github.io/
• Pytorch’s official quickstart tutorial