Think of the set of questions one can ask about data as living on a simplex, where the vertices represent Confirmatory questions, Exploratory Questions, and Predictive questions about the data. Here is a visual aid I've taken from a course my supervisor has taught. Included are some questions that could be asked about data concerning how many people are in our university's gym at a given time on a given day.
One way to think about statistics vs ML is by partitioning the simplex as so
I think this is a good way to think about the difference between statistics and ML. As for statistical learning, I would put this somewhere in the purple region; methods for prediction or data mining which seem to be motivated by traditional statistical tools. The definition is highly variable and dependent on who you ask. Consequently, the distinction is of little practical importance.