I'd agree that Tech has too few TAs for too many students, generally, for its graduate courses, but I don't know that other schools do a better job. A brief survey of the folks around my desk elicited howls of laughter at the notion of useful or accessible TAs in grad school.
> I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about.
I think this assertion is, at best, too strong. A better assertion might be that his lectures depended on coming in with sufficient background.
As I said, I came into the course with no experience with machine learning at all. On the other hand, I did have a fairly strong theoretical computer science, stats, and linear algebra background. I will admit that may have made me blind to things he was simply assuming with respect to educational background that were not actually safe to assume. That said, I still refer back to his primer on information theory (http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf) when discussing work relying on it, so he certainly made some effort to fill in gaps as he discovered they were common.
> When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student.
For a graduate level course I feel a class clears this bar when it accurately and thoroughly documents the prerequisites. Now, I'm not saying Charles's class necessarily does this. As I said, I came in with a pretty strong background in what turned out to be more than sufficient, but with that background I personally felt his lectures were quite tractable, even assuming complete ignorance of ML itself.