Which specific Coursera and/or Udacity courses are good for coming up to speed on the math/stats&probability knowledge needed for their SDC/AI/ML courses?
For math/stats&probability in the specific problem domain of autonomous vehicles (of which self-driving cars are a subset) I'd recommend no other than Sebastian Thrun's Udacity course "Artificial Intelligence for Robotics" [https://www.udacity.com/course/artificial-intelligence-for-r...]. The content is practical, and Thrun, in my opinion, is great at breaking down complicated topics (e.g. Kalman filters) into simpler sub-components and explaining the intuition behind them. You'll get a heavy dose of math/stats&probability in the context of autonomous robots (and in certain cases, Thrun contextualizes to his work in the DARPA grand challenges while at Stanford, which laid the groundwork for his work at Google on the self driving car project [now Waymo]). I can't speak to how much the "traditional" localization, mapping, and planning techniques taught in the course crossover to deep learning approaches, but you'll no doubt get the math/stats&probability knowledge you're looking for, in the context of self-driving cars.
A linear algebra course is the bare minimum (and frequently it can be enough). You can pick up any textbook and that would give you good enough preparation.
After that you probably will have enough domain knowledge to choose what course to take next yourself.
If I don't really remember what all was in my Linear Algebra class from 15 years ago, but have a working knowledge from graphics would that work, or would I need more of a theoretical background in linear algebra do you think?
