I agree - it looks equivalent to perhaps 1st year mathematics in Australian university or 2nd year 1st semester if I'm being really generous.

This definitely isn't sufficient for machine learning, but it is a start.

Ah, I should've figured that out myself from looking at the contents. I might try it out and combine it with some reading of my old statistics book or some other means. Thanks!

My impression from reading about a few ML techniques (such as Neural Nets or Support Vector Machines) is that this class of ML algorithms relies heavily on regression techniques that are, at their core, nonlinear optimization problems. This means finding local maxima and minima for systems of nonlinear equations, either analytically or through gradient descent. Either way, you're going to need to deal with a system of partial derivatives, which means you need to understand vector calculus and linear algebra. If that's the focus of this course, then I'd say it does make sense.

I understand what I described above is a subset of ML, and I generally do agree with you that a solid background in probability and stats is important for people who plan to do a lot of ML.

Note - another possible objection here is that all "STEM" fields require calculus through differential equations and linear algebra. That's pretty much the common thread for most majors generally grouped together as STEM fields. So calling this "mathematics for machine learning" could be a little strange. If we're going to call vector calc and linear algebra "mathematics for ML", why aren't we calling it "math for physics", or "math for engineering"... I think that part of what is going on is that ML has gotten very popular, and people are starting to ask what the essential math background is, and are discovering that it's, well, pretty much the two year science and engineering track calculus you'd take at most universities.