Introduction to Computer Vision

I think this is the best computer vision MOOC as it covers almost all the traditional computer vision techniques (as opposed to deep learning). This is a proper graduate-level course with rigorous mathematics and the instructor has good sense of humour too. My advice for or students planning to take this course is, after the first few lessons, you don't need necessarily need to follow the course order, just jump the the topics you're interested in or you might not finish the whole thing. I have been following this course on and off for 3 months but still yet to finish half of it.

Simply the best course I have ever taken. The content is interesting, the presenter is engaging and the concepts are explained clearly and intuitively. Mixed through the lectures are matlab/octave exercises which are really great for checking understanding of the lecture material and give a good amount of practice in the practical application of the knowledge you are getting. Really wish some of my engineering lectures at university could compare to the quality of this course.

Its the best quality content available free for computer vision. Some tips are: if you need some reference then only download the pdf of the coursebook it is not worth buying. I purchased Forsyth and ponce and it was a total waste of money. The book is wayyy too hard. So do it . In the initial lectures( module 2- 3) do it at a slightly fast pace because if you do it slowly you might lose interest in the course.

Prof. Aaron Bobick makes the class interesting with his humor. He generates interest for people by covering the topics at a faster pace yet in a way understandable for newbies.