Obviously there are overlaps between econometrics and machine learning.
My question is: to what extent does doing graduate level econometrics actually overlap with machine learning? I mean this specifically in the sense: to what extent does doing graduate econometrics help you build either the specific concepts/methods, OR just build the general underlying math understanding that is useful for machine learning?
Another way of saying this would be: if you're interested in ML, but you can't take an ML course, to what extent is taking an econometrics course worth it?
A possible answers could be:
graduate level econometrics is for about 40% the same as machine learning, for another 30% it is different but still concerning underlying math that helps you understand ML, and for another 30% totally irrelevant. This is so because ....
if you do graduate level econometrics on topics X, Y, Z, you will only have to spend 20% of the time to learn ML topics U, W, V, compared to someone who didn't do the econometrics. This is so because...
By graduate level econometrics I mean stuff like: time series and panel data, estimators and test statistics for them (GMM, maximum likelihood, proofs of efficiency and consistency results, ...), both introductory and advanced graduate courses.