> ML is a discipline that has existed for several decades
If you're going to say regression is part of ML, you can't say ML has only existed for decades. If you define it with that expansive scope, it's existed for centuries.
ML uses regression in a particular way with particular aims and has developed prior approaches like OLS and Ridge regression, lasso, kriging etc. into a particular theoretical framework and analyses them from the angle of learning, i.e. heavy focus on generalization from training data to test data.
The truth is, these are tools that many communities have used in parallel and these communities are in constant flux regarding what promising directions they find and exploit and when they make a big splash in one place, others take notice and incorporate those ideas etc. There are no rigidly predefined disciplines and fields over long timescales.
When electrical engineers and signal processing communities worked on similar things they called it "pattern recognition".
Machine learning as a field started out with more ambition than mere regression and classification (and indeed covers a lot more ground), but it turns out that supervised learning has had the most practical success. But it's a research program and community that goes beyond that.
Similarly, there are parallels and similar equations between control theory and reinforcement learning. And indeed some controllers can be expressed as reinforcement learning agents. But the aims of the two communities are not the same.
Maybe people would be happier if "statistical learning" (which is also used) was used more instead of "machine learning"? But indeed as another comment points out, ML as a paradigm does not necessarily require learning of a statistical kind.
Labels grow and wane in popularity, it doesn't mean it's the same thing repackaged, rather that the aims and the focus changes depending on what we find most productive and fruitful.
For example many of these things were also called "soft computing" a few years ago, but that term is rarely seen nowadays.
This sounds like in the end you agree that a lot of ML is applied stats?
The problem in my mind is not that ML is using a lot of stats (obviously), it's that foundational mathematical concepts get labelled as ML techniques. This is why the title of the post is so annoying. This totally obscures the structure of the field. E.g. I wouldn't call linear algebra a quantum mechanics technique. I would say that QM uses (and spurred the development of) a lot of LinAlg.
Well, if ML people didn't use the word "regression" and named their use of it differently that would also upset stats people.
The point is, when you listen to an ML person introduce regression in a lecture it will look and feel different from when a stats person does it. ML-type regression is part of ML. Stats type regression doesn't cut it. They care about different aspects, flesh out stuff that's not very relevant for ML and ignore parts that are more important for ML.
> ML is a discipline that has existed for several decades
If you're going to say regression is part of ML, you can't say ML has only existed for decades. If you define it with that expansive scope, it's existed for centuries.