Patrick teaches 6.034, the undergraduate introduction to AI at M.I.T. and a recent set of his lectures is available as videos.
I want to point people to lectures 12a and 12b (linked individually below). In these two lectures he goes from zero to a full explanation of deep learning, how it works, how nets are trained, what are the interesting problems, what are the limitations, and what were the key breakthrough ideas that took 25 years of hard thinking by the inventors of deep learning to discover.
The only prerequisite is understanding differential calculus. These lectures are fantastic. They really get at the key technical ideas in a very understandable way. The biggest network analyzed in lecture 12a only has two neurons, and the biggest one drawn only has four neurons. But don’t be disturbed. He is laying the groundwork for 12b, where he explains how deep learning works, shows simulations, and shows results.
This is teaching at its best. Listen to every sentence. They all build the understanding.
I just wish all the people not in AI who talk at length about AI and the future in the press had this level of technical understanding of what they are talking about. Spend two hours on these lectures and you will have that understanding.