Looks like he used a hierarchical clustering (probably single-linkage) rather than K-means.

But yes, I'm also not too keen on using a clustering algorithm and calling it machine learning.

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?

Well, where's the 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)

Machine learning gets more eyeballs than k-means clustering.