A college level approach could look at the line between Math/Science/Physics and Philosophy. One thing from the article that stood out to me was that the introduction to their approach started with a problem about classifying a traffic light. Is it red or green?

But the accompanying XY plot showed samples that overlapped or at least were ambiguous. I immediately lost a lot of my interest in their approach, because traffic lights by design are very clearly red, or green. There aren't mauve or taupe lights that the local populace laughs at and says, "yes, that's mostly red."

I like the idea of studying math by using ML examples. I'm guessing this is a first step and future education will have better examples to learn from.

> traffic lights by design are very clearly red, or green

I suspect you feel this because you are observing the output of a very sophisticated image processing pipeline in your own head. When you are dealing with raw matrixes of rgb values it all becomes a lot more fuzzy. Especially when you encounter different illuminations, exposures and the cropping of the traffic light has noise on it. Not saying it is some intractably hard machine vision problem, because it is not. But there is some variety and fuzzyness there in the raw sensor measurements.