Lectures will loosely follow the content in the following book (referred to as SZ in the lecture schedule page), available for free download:
Szeliski, Computer Vision: Algorithms and Applications, 2022 (online draft)
In addition, the following books may be useful:
Goodfellow, Bengio, Courville, Deep Learning, MIT Press, 2016
Hartley and Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004
Forsyth and Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002
Palmer, Vision Science, MIT Press, 1999
Content from related courses at other universities may also be useful:
Advances in Computer Vision, by Bill Freeman and Phillip Isola
Introduction to Computer Vision, by Michael Black
Learning-Based Methods in Vision, by Alyosha Efros
Computer Vision, by Kristen Grauman
Computer Vision, by Rob Fergus
Introduction to Computer Vision, by Fei-Fei Li
Sample Colab notebooks:
Colab and PyTorch Tutorials:
Colab Tutorial 1
Colab Tutorial 2
Colab and PyTorch
PyTorch
Basics of PyTorch
PyTorch Tutorial
Deep Learning 60 Minute Blitz with PyTorch