Is it less ML than linear regression?
In some sense the exact place you draw the distinction is arbitrary. You could try to characterize where the distinction is by saying that models with fewer parameters and lower complexity tend to be called “not ML”, and models with more parameters and higher complexity tend to be called “ML”.
If you want to say "not neural networks" or not dnn or not llm, sure. But it's obviously machine learning
There are pedagogical reasons why you’d include linear regression in a machine learning course. This is pretty clear to me—they have properties which are extremely important to the field of machine learning field, such as differentiability.
All of these are machine learning techniques. Doing it by hand doesn't change anything. Today we use machines so it's machine learning
See: "Program".
But, other than that there’s the only clear line is when the programmer isn’t hard coding results which puts Linear regression over the ML line. I guess you could argue about supervised vs unsupervised algorithms, but that’s going to exclude a lot of what is generally described as ML.
> Linear regression is also a type of machine learning algorithm, more specifically a supervised algorithm, that learns from the labelled datasets and maps the data points to the most optimized linear functions that can be used for prediction on new datasets
You can pretend it's not because it's not a sophisticated machine learning algorithm, but you are wrong.
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.
“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.
Anything that can separate data apoints can rightly been seen as a "supervised machine learning classifier".
Todemystify the area, I literally introduce my intro to ML lecture by drawing a line on the board, give its equation y = 0.5 x on the backboard, reminding students that they already know this, and then explain how to use it as a spam filter by interpreting the points on either side of the line as good emails versus spam ones.