==== Textbooks ====

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. 
  * [[http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf|Online version]]
  * [[https://sites.google.com/site/szeliskijp/book/eratta|Errata]]

Supplementary (Optional):
  * Multiple View Geometry in Computer Vision, Hartley R & Zisserman A, 2004
  * Pattern Recognition and Machine Learning, Bishop CM, 2006
  * [[http://www.computervisionmodels.com/|Computer Vision:  Models, Learning and Inference, Prince SJD, 2012]]

==== Tutorial References ====

Linear Algebra:

  * For an introductory reference to linear algebra, I highly recommend the classic book by Gilbert Strang: [[http://math.mit.edu/~gs/linearalgebra/ |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 [[https://matlabacademy.mathworks.com/ | introductory MATLAB courses]] 

  * There are also numerous tutorial documents available online [[https://www.mccormick.northwestern.edu/documents/students/undergraduate/introduction-to-matlab.pdf | (example)]]