Hi r/ml. I hope this isn’t the kind of post that is unwelcome here but I thought maybe you guys could help give me some pointers. I am an undergraduate senior and I will be spending this year writing a capstone math thesis. I need to identify topics that I can propose to my department. They need to be topics that are in the scope of classical statistics, algorithmic analysis, or probability. It can’t be something that is more the domain of computer science, like network architecture.

I was wondering if there are any topics you guys think are important or foundational to other concepts that are important in ML. I spent the last summer researching variational autoencoders and became familiar with variational expectation maximization. This is something I’m excited about, but I’m looking for other ideas as well.

I guess another way of asking this question is: what are the parts of papers that you wished you knew more about? What math concepts come up a lot and are hard to understand?

Thanks for the help, and again sorry if this is the wrong place to ask.