if the aim is to just read and understand the papers, it is just a matter of learning the ap calc and some first-year university maths to get the basics out of the way.
the rest of the journey is to find an ml course which acts like a survey of the current state of the art. this field has complexity due to abstraction and horrendous naming practices. to understand a given paper requires working your way in reverse from concepts around it.
in addition, learn a "maths in code" platform of your choice to map the concepts to something you can run.
I feeling like you described a “draw the rest of the owl” situation with “find an ml course”.
I’m basically in this situation, I’ve implemented products around AI and ML. I understand them at a practical level but want to dive into the theoreticals more. Finding a “survey ML course” is a huge challenge for me. I have absolutely no idea what’s considered state of the art.
however, the strategy that should give good results is going for the open-source coursework from one of the major universities. these courses may lag a semester or two from SOTA, but they often give a good overview. pick your poison and go from there. for example the above link was found the same way.
the rest of the journey is to find an ml course which acts like a survey of the current state of the art. this field has complexity due to abstraction and horrendous naming practices. to understand a given paper requires working your way in reverse from concepts around it.
in addition, learn a "maths in code" platform of your choice to map the concepts to something you can run.