After having my first Linear Algebra course I came across the online course called Linear Dynamical Systems by Prof Stephen Boyd of Convex Optimisation fame.
Every lecture was so eye opening. I couldn't believe that linear algebra could be taught in such a context with such a variety of application domains.
Pick one university and do their core CS track with the materials available. Like CMU, Berkeley, MIT, Stanford, etc. Then start building stuff and learning from more advanced CS and math courses/books/papers.
He is not a Twitter bro though. He has developed software extensively. He is a PL researcher, a professor at BrownU, now working in computing education.
I see many people dive into ML research by tinkering along the way. Although I have no objection to particular tastes, two courses that made modern ML easy for me were:
It is very hard to pinpoint a single moment. Rather I would like to list a few books that I feel showed me that mathematics can be beautiful and interesting.
1. Measurement by Paul Lockhart
2. An Infinite Descent into Pure Mathematics by Clive Newstead (available @ https://infinitedescent.xyz/). It taught me basic discrete math and proof writing.
3. Apostol's Calculus Vol 1. It is just so beautiful.
I don't know about mathematicians, but everyone should read How to Design Programs book when they are starting out. Fantastic way of forming great mental models.
Why aren't more universities teaching functional programming first to form good mental models for the students and then show them when to use state and when to not.