Thanks. I am passionate haha. I do like to educate, but it is often difficult to do on these forms as I do not know peoples backgrounds. If you throw too high level at them, I find it does more harm than good. I also find that while I know many here took things like calculus, that we also need to recognize that skills degrade with time (though can be regained more easily).

I do understand that it is difficult to find the math pathways in ML. What I do suggest is focusing on generative models[0], but the important part is to keep asking "why" and "what does this actually mean?" I think it is too easy to get stuck in knowing the answer and accepting it without understanding that there is no "right" answer, but less wrong answers. The path gets clearer if this attitude is adopted. This is where I've found teaching highly beneficial[1], as you face the basics and you'll find many questions that are easy to dismiss. To be a good teacher you have to take dumb questions seriously to determine if they're actually dumb or "dumb"[2]. Things you probably asked when learning: "how many layers?", "how many neurons per layer?", "is the optimization space actually smooth like the 2d decent graphs we saw?", "does data lie on a manifold?", "what is a manifold", "does data always lie on a lower dimensional manifold?", "is data always a distribution?", "is my data actually representative of my goals?", "what does this measurement mean?", "what does this measurement not tell me?", and so on. These are all extremely important questions that are almost universally ignored, but that all appear to have simple answers.

The reason I do like category theory (Bartosz also has video lectures for those), is because it helps connect the many different disciplines of math that are needed to answer some of these. To see the generalizations of things like surjective (epimorphic) and injective (monomorphic) functions, plays a role in answering the layer and neuron questions. It was the way I could start understanding how field theory wasn't just cool but impractical math.

But to ML, I think there's this hard gap and I'm not sure of a good resource that fills it (I'm working on one myself). That there's lots of basics (blogs like "the math behind transformers" that show the equations and little to no more) and there's also plenty at a high level by experts in cat theory, set theory, algebraic geometry, or others. The former aren't very useful and the latter can be easily found if you have the requisite knowledge but are impenetrable if you don't.

But with diffusion models and score matching being all the rage now, I highly suggest reading into Aapo Hyvärinen's[3] work. At lower levels I suggest Gelman's book and/or McElreath's. Needham has impressive illustrations and writes so well that you can only emulate because he's at a different level. I found Shao's Mathematical Statistics greatly helpful, but this is not easy to parse. Gallier and Quaintance are also worth looking into. But if you need something on the easier side, Tomczak is your friend. On the YouTube side I'll recommend some that are more easily missed: mathemaniac, EpsilonDeltaMain, jHan, ron-math, and Mutual_Information. You should find more from these too. Also search the #some{,2,3} tags, there's a lot of hidden gems. There's also a Cats4AI group btw, and many of them have now created a startup.

[0] Realistically all models are generative. The definition is ill-defined and you can even demonstrate that classifiers are EBMs. https://arxiv.org/abs/1912.03263

[1] I'm finishing a PhD program, so I do teach a ML course

[2] In either case you have to answer nicely. But it is easy to trick yourself into thinking something is simple when it is not.

[3] https://www.cs.helsinki.fi/u/ahyvarin/papers/