The required textbook is: Computer Vision Algorithms and Applications, Richard Szeliski, Springer, 2011.

• Hardcopy: available at the York Bookstore; one copy is on reserve at the Steacie Library.
• Online version
• Errata

Supplementary (Optional):

• Multiple View Geometry in Computer Vision, Hartley R & Zisserman A, 2004
• Pattern Recognition and Machine Learning, Bishop CM, 2006
• Computer Vision: Models, Learning and Inference, Prince SJD, 2012

Linear Algebra:

• For an introductory reference to linear algebra, I highly recommend the classic book by Gilbert Strang: Introduction to Linear Algebra, Strang G, 2016

• Appendix C of the Prince textbook (see above) provides a nice succinct of fundamental linear algebra methods commonly used in computer vision.

• Appendix C of the Bishop book (see above) provides a summer of useful properties of matrices.

• Appendix A of the Szeliski textbook provides a review of some of the linear algebra methods used in the book.

MATLAB:

• Mathworks provides a series of free introductory MATLAB courses

• There are also numerous tutorial documents available online (example)