Is there a reason you have "Machine Learning" courses before "Artificial Intelligence"? Stanford CS 221 is (or was recently) a broad, shallow overview of AI topics (including machine learning). I think many people would have taken it before any 22_ courses (like 229, Machine Learning) or just skipped it. I don't think these courses have changed too much, and the equivalents from other schools look to be in a similar situation.

Also perhaps be careful with the Stanford undergrad CS theory courses. CS 103 and 109 are required for the major; they're definitely about things worth knowing, but they are taught to very large audiences with very little mathematical maturity. Math/physics people might want to be prepared to at least supplement the courses with some self-study. If you can learn enough discrete math and probability on your own to jump to graduate courses in those subjects, the department should be flexible about substituting those as major requirements. CS 161 (Algorithms) is also required, but I'd recommend taking it regardless of your math background. It can be easy to fool yourself in that subject.

Also, two years ago CS 154 (Intro to Automata and Complexity Theory, not a required course for the major) was basically a repeat of 103 minus the logic. Does anyone know if it has improved?

Stanford's Math 113V (Linear Algebra) is a summer course; Math 113 is the usual (non-applied) linear algebra course.