For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one with a tight generalise/specialise loop.
But the scasp finds the two potential rules that cover both positive examples but not the negative example.
One difference is that in Machine Learning you must think of data structures and algorithms. i.e. the practical ways to compute a model. How to represent and transform data while building a model. I think this is given less emphasis in statistics. Standard models are often used and theory is built around these different models. For example aspects such as power calculations for a regression model.
One of the interesting things about this take on Prolog vs the Power of Prolog (https://www.metalevel.at/prolog), is that it attempts non-monotonic reasoning. There is still a lot of value in the ideas of abductive and inductive logic programming that have not been fully exploited in the current machine learning trends.