ML and statistics are a subset of maths. As I said the statistics and ML overlap and also function interpolation. But some ML algorithms are based on biological systems (like swarm optimization), or theory of information.

If you have a problem that you want to classify some vectors, you have different ways to do it. You call all of them ML, but some use statistics, others use interpolation, other uses theory of information, etc. The model doesn't have to be complex or require a lot of data. Instead of saying all the different techniques you sum up saying ML.

Indeed.

A lot of it went over the head because I don't know much classical statistics, but I read some articles by stats people that basically boiled down to the distinction not being in the techniques but in common assumptions, rigor, culture, etc.