You ain't wrong.
Lots of ML is heavily influenced by fundamental research done by Physicists (eg. Boltzmann Machines), Linguists (eg. Optimality Theory / Paul Smolensky, Phylogenetic Trees/Stuart Russell+Tandy Warnow), Computational Biologists (eg. Phylogenetic Trees/Stuart Russell+Tandy Warnow), Electrical Engineers (eg. Claude Shannon), etc.
ML (and CS in general) is very interdisciplinary, and it annoys me that a lot of SWEs think they know more than other fields.
Having studied control engineering, it feels like ML is control theory + optimization all the way down :)
I love how folks from different backgrounds can interpret it in so many ways.
ML is remembering that computers can do math.
It’s also signal processing.
Think of this as a Nobel prize for systems physics – essentially "creative application of statistical mechanics" – and it makes a lot more sense why you'd pick these two.
(I am a mineral physicist who now works in machine learning, and I absolutely think of the entire field as applied statistical mechanics; is that correct? Yes and no: it's a valid metaphor.)