No idea why this was downvoted; I find this to be an accurate description. The difference between stats and ML is mostly one of terminology and perspective. I studied math in college and grad school, and there were several moments in Andrew Ng's online lectures where I thought "oh, I know this, but we didn't call it that, and I had no idea it was considered ML."

reminds me of the whole Bayesian perspective of ML

Or simply statistical modelling. I think there are methodological differences though. To me ML seems like a massive p-value fishing operation.

Do you mind elaborating? I can't think of any methods that explicitly (or implicitly) use p-values. The only place in the industry I have seen p-values used is AB-testing, but most seem to be trying to move to a multi-armed bandit, bayesian methodology.