ya lol and Hamiltonians. sometimes people just reel off all the math they've heard of to sound impressive. next we'll have people talking about de rham cohomology because of TDA (or something like that)
Additionally, we're seeing the introduction of even more sophisticated stochastic samplers (stochastic gradient hamiltonian monte-carlo, etc) that require even more esoteric branches of math and physics to really grok. I have a strong math background but frequently find myself struggling with a lack of knowledge in statistical mechanics when trying to read papers in these areas.
So yeah - there's plenty of bullshit and exaggeration. But there's also some wicked cool stuff happening which requires very sophisticated (and specialized) knowledge to understand.
I've been working in golang, which fortunately has built-in complex128 types, so it's proved very helpful in a project!
I took a complex analysis class and did OK, but I get the feeling that EEs are the ones who really benefit from it (at least in the applied world). They seem to have some very rich analyses of linear dynamical systems using frequency domain methods.
I believe the above was used in sk-learn or PyMC3 at some point.
Like I said, the list was a bit too theory focused and not just for ML. Hope that clears things up.
Also, maybe a bit out of place, but it makes my day happier when I assume good intentions out of random tidbits posted online.