[rm999]

I disagree with a few things about your comment. Your criticism of machine learning feels off-base, and is too specific to describe such a wild field. Who sells parameters? What does that even have to do with whether it is a science? I can think of other fields where every detail of an experiment isn't spelled out in every paper.

It's hard to separate machine learning and statistics because so much of machine learning derives directly from statistics. Motivation is probably the most important distinction; machine learning is applied statistics. I'd say it's a mix of science (the scientific method plays a big part in model building for example), engineering, and math. Statistics is first and foremost a branch of mathematics, not science; the scientific method does not play a role in the vast majority of the field.

[alook]

> machine learning is applied statistics

It really is hard to separate ML & statistics - any competent practicioner of ML appreciates the statistical achievements that made Machine Learning methods possible. And statisticians must understand that to help automate decision-making systems, using learning methods/boosting is a viable option.

The debate around nomenclature (ML/stats/AI) seems limited to the academic community. Most data scientists I've met tend to accumulate a repertoire of tools from different fields, rather that side with either Machine Learning or Statistical communities.