What do I need to learn in order to really understand Machine Learning?
From somebody with a PhD in Probability working with AI/ML for a living. Basics of Probability theory, maybe wikipedia/cousera/... for the very basics if you never had a class in it, followed by e.g. “Probability with Martingales” by Williams.
The papers here will also give you a good feel.
As for books, this one on “classical” machine learning is pretty good and free For Deep Learning https://www.deeplearningbook.org/, this one also has the basics of probability theory.
For reinforcement learning http://incompleteideas.net/book/the-book-2nd.html.
For the applied side, slides from https://web.stanford.edu/class/cs224n/ and http://cs231n.stanford.edu/.
As for statistics don’t bother, I’ve never seen any machine learning work reference a theorem in “pure” statistics, if there is such a thing.
Of course standard undergraduate calculus and linear algebra. And learn some Python while you’re at it