EECS 442: Computer Vision (Winter 2022)

S is Computer Vision: Algorithms and Applications by Richard Szeliski, which can be found here. The chapters refer to the first edition. This will be more accessible.

H&Z is Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman, which can be obtained via the library in electronic form (scroll past the physical copies). I'd recommend this only if you're feeling adventerous.

ESL is Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, which can be found here (PDF). This is relatively accessible.

Kolter is Zico Kolter's linear algebra review and reference note here (PDF). For the purpose of 442, feel free to skip ‘‘Determinants’’, ‘‘Quadratic Forms and Positive Semidefinite Matrices’’ (although this is good to know), ‘‘The Hessian’’, and ‘‘Gradients and Hessians of Quadratic and Linear Functions’’ (until we hit deep learning), and ‘‘Gradients of the Determinant’’.

Re-use policy: I am extremely grateful to the many researchers who have made their slides and course materials available. Please feel to re-use any of my materials while crediting appropriately and making sure original attributions to these generous researchers is preserved. Please consider making your own course materials public.