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In the same way that the physical reality we can measure is the ultimate referential for physics research, the real-world effectiveness of our algorithms and systems is the ultimate referential for machine learning research.
Machine learning is not mathematics (if it were, we would call it mathematics)
ML = optimization + statistics /subset mathematics, but go ahead and define ML as you like.
And aeronautics is differential equations + trigonometry?
ML for the most part, if not entirely, is heavily driven by mathematically-based and statistical approaches and concepts. That is what I see out there now...
My view on ML is that it is much broader and more complex than just an engineering field or sub-field ...
Hallo there is your unroll: https://threadreaderapp.com/thread/1018397820615823360.html …
Enjoy :) 
The Theory of statistical learningn(VC dimension etc..) is as close to pure science as it gets for ML. Everything else is a mixture of science and engineering
Too many programmers wrongly call themselves Software Engineers. Real Engineering is reality-based but software is not physical, though its substrate is, & in many ways, some software can be classified Engineering, but void of Full recognition of reality. It's the Bridge where...
Everything is mathematics if you want to be that guy.
It is not mathematics in a sense that it is highly experimental. It is closer to engineering or exact sciences, where you heavily use mathematics as a tool, but you are not interested in proofs or pure math ideas.
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