Ooh, that's a great suggestion! So one thing I learned a lot of in my PhD (for literally a whole year), that I literally never needed, was functional analytic

Ooh, that's a great suggestion! So one thing I learned a lot of in my PhD (for literally a whole year), that I literally never needed, was functional analytic methods for PDEs. Stuff like Moser iterations / the De Giorgi-Nash-Moser theorem, etc. The finer details of Turing machines have never really helped me, although in my case that's probably the exception as I imagine that's still pretty important. On a more ML note, I have literally never needed SVMs. (And hope I never get asked about them, I've forgotten everything about them haha.) I think there's a lot of other stuff I could add to the "just-don't-know-stuff" list! (And to answer your last question: this list is curated, and based on the criteria of (a) is it useful, and (b) is it widely applicable.)