Course resources

Textbooks

There is no required textbook for this class. Nevertheless the following are useful computer vision references:

• Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2nd ed.) (optional)
• Computer Vision: Algorithms and Applications, by Richard Szeliski (online copy).

For each lecture we will post links to the relevant sections of Richard Szeliski's (RS) book.

And these for machine learning:

• Machine Learning: a Probabilistic Perspective by Kevin P. Murphy (Amazon link) (optional)
• The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman (optional)
• Pattern Recognition and Machine Learning, Christopher M. Bishop (optional)
• Machine Learning, Tom M. Mitchell (optional)
• A Course in Machine Learning, Hal Daumé III (available online)
• Neural Networks - a Systematic Introduction, Raul Rojas (available online)

Programming and background

• Matlab resources
• Obtaining Matlab from the university
• NumPy tutorial from Stanford CS231n
• NumPy for Matlab users
• Vector geometry notes by Denis Sevee
• Linear algebra review (via David Kriegman)
• Random variables review (via David Kriegman)
• Matconvnet
• Pytorch
• How to write a good homework report

Past offerings of 670 at the university

• Fall 2018, Instructor: Subhransu Maji
• Fall 2017, Instructor: Subhransu Maji
• Fall 2016, Instructor: Subhransu Maji
• Fall 2015, Instructor: Subhransu Maji
• Fall 2014, Instructor: Subhransu Maji
• Fall 2013, Instructor: Erik-Learned Miller

Related courses at the university

• CMPSCI 682: Neural Networks: A Modern Introduction
• CMPSCI 590: Intelligent Visual Computing
• CMPSCI 373: Introduction to Computer Graphics
• CMPSCI 370: Introduction to Computer Vision