Quick question I guess, but is there a perceivable difference between the terms Statistical Learning and Machine Learning, or is it simply area jargon? I gather the computer scientists like to refer to machine learning while statisticians might refer to statistical learning (no less influenced by the famous book).
This motif repeats across other questions in the site as well, with many questions like "what's the difference between machine learning and something else", but I'd like to have this one specifically answered (with some references if possible) to solve the possible merge of the terms.
This question is rooted in a recent question on meta regarding The *learning tags, where SL and ML were agreed to be made synonyms (refer to my answer for some background and what I have gathered on the subject so far).
Quoting my answer:
Perphaps the difference is simply cultural, like many discussions in the main site pointed. Consider Stanford, where two courses are taught: Stats 315a/315b - Statistical Learning and CS 229 - Machine Learning. Apart from being named different and being in different concentrations areas, they also attract different students.
Tibshirani even shares his views in his page comparing both courses and then both terms:
Machine learning research focusses more on low noise situations, eg engineering applications like robotics and physical sciences
Statistical learning focusses more on high noise, observational data like medicine and genomics, and problems where interpretation of the fitted model is important
But more and more overlap in application areas!
