Leading universities like Stanford teach k-means clustering in their "machine learning" courses. Why do you think they're wrong and it's not actually machine learning?
There's a general tendency to reclassify any kind of statistical analysis that produces a useful result as "machine learning", and I don't think it helps. For example there are lots of machine vision techniques; the neural net ones can sensibly be called "learning" but things like edge detection shouldn't be.
(also, just because something is taught in course X doesn't mean that it is X, it might be an embedded prerequisite)
There's a general tendency to reclassify any kind of statistical analysis that produces a useful result as "machine learning", and I don't think it helps. For example there are lots of machine vision techniques; the neural net ones can sensibly be called "learning" but things like edge detection shouldn't be.
(also, just because something is taught in course X doesn't mean that it is X, it might be an embedded prerequisite)