The Math section as a whole seems very useful, especially for the self taught engineers (or people who need a quick memory refresh of the underlying subjects). The concepts are simplified enough that anyone with basic understanding of numbers can just jump in and learn what they might need regarding this particular field.
I wonder whether something like this (albeit a little advanced, considering the field's depth) exists for machine learning?
Andrew Ng's Coursera course "Machine Learning", probably fairly well know at this point, is fairly entry level and cover the basic math and theory of machine learning and some deep learning. If that answer your question. Although the content is probably a mix of video and pdf (exercise material)
There's also "Grokking Machine Learning" by Serrano, which I haven't read, but seems relevant to your question as well
> Discover valuable machine learning techniques you can understand and apply using just high-school math.
I don't think such a thing exists. Math for physical modeling for game dev is just the simpler aspects of linear algebra, which builds off of basic trig and algebra, stuff that most people learn by early high school. It's also simulating the real world, so it's easy to build intuition that way.
ML math, depending on how far you want to go, builds off of statistics, probability, linear algebra, basic calculus, information theory. There's more things that it builds off of, and they're more advanced topics that people don't get as much exposure to without going out of their way. ML is usually a lot harder to build intuition about, since so much of it doesn't have real world analogies that can easily tie together the math with the application.
There's a lot of individual pages scattered around the web that try to explain specific concepts in ways like this godot vector math page does (gradient descent tutorials come to mind), but there's no central repository or unified intuition because the scope is so broad. As the other guy mentioned, things like Andrew Ng's course are probably the best you're going to get if you want a decent understanding of the concepts.
I wonder whether something like this (albeit a little advanced, considering the field's depth) exists for machine learning?