[windsignaling]

After spending over a decade in both statistics and machine learning I'd say the only reason there isn't a "broad consensus" is because statisticians like to gate-keep, whether that's linear regression, Monte Carlo methods, or Kalman Filters.

Linear regression appears in pretty much every ML textbook. Can you confidently say, "this model that appears in every ML textbook is the only model in the ML textbook that isn't an ML model"?

Kalman Filters are like a continuous-state HMM. So why are HMMs considered ML and Kalman Filters not considered ML?

IMO it's an ego thing. They spent decades rigorously analyzing everything about linear models and here come these CS cowboys producing amazing results without any of the careful rigor that statisticians normally apply. It's difficult to argue against real results so the inflexible, hard-nosed statisticians just hang on to whatever they can.

[klodolph]

That’s unfairly harsh to statisticians, IMO. You have two fields of study, statistics and ML. There’s a massive overlap. Gatekeeping? Practitioners from these two fields have different jargon and view things differently from each other.

“X is taught in books about subject Y” is a pretty weak argument. I could use it to argue that group theory is quantum mechanics. Scientists and mathematics aren’t fighting over who gets to own group theory—the scientists get to put group theory in their toolboxes and the mathematicians get to study it for itself. Same with ML and statistics. When you do ML, you need certain statistical techniques in your toolbox, so they get taught in your ML books.