That's Stage 1: Foundational Math -- with the addition of calculus-based probability/statistics, and the specification that it's the multivariable chain rule (not just the one in single-variable calculus), as well as differential multivariable calculus in general (gradient vector, etc.).
But there's plenty more to learn in stages 2/3/4 before you reach the cutting edge. Would encourage you to read the article! :)
Should also point out that -- in the USA, at least -- linear algebra, multivariable calc, and calculus-based probability/statistics are not typically taught in high school. There's quite a bit of university-level math that someone needs to know if they want to reach cutting-edge ML/AI.
Not many. I went to a good school but still had to wait to first-year university to learn linear algebra. Assuming you mean "real" linear algebra with matrices, inverse matrices, determinants (please don't mention Axler's book), and linear operators.
I believe Linear algebra is/was taught in the pre-calc path of the provincial public curriculum (Canada) if you chose it as an optional thing. I did not.
It's common to find a little bit of linear algebra in a serious precalculus course, but nowhere near what's needed for ML. (For instance, I've never seen a precalculus course that covers eigenvectors, diagonalization, SVD, pseudoinverse, subspace projection, etc.)
