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The sad truth of the matter is that ML is more applied research at this point than a sensible set of programming problems. From that standpoint, graduate mathe
by arcanus 10y ago
The sad truth of the matter is that ML is more applied research at this point than a sensible set of programming problems.
From that standpoint, graduate mathematics is more useful for a practitioner than any robust programming experience.
- argonaut 10y agoML involves math. That does not mean it's "applied research," though. The math is mostly at the undergraduate-college-level, and is mostly applied math - except for very theoretical ML/statistics which a practitioner can easily avoid. The math involved straddles an awkward boundary where most undergrad math majors find the math quite simple, but most CS majors would think it's too much math.
- visarga 10y agoThere are two major cases: academic, related to algorithm design and industry - related to deployment of already existing algorithms on various data sets. For a CS engineer who wants to be able to use the latest Inception neural net from Google in his pipeline, there is actually almost zero math need. It's like any other API. In goes the image, out comes the label. What she would need to know, as a good utilizer of ML, is just a bunch of concepts, such as training/test/validation, bias/variation, how to extract features from data and how to select a good algorithm and framework. So it's mostly data cleaning and tuning hyperparameters, the latter of which can be learned by trial and error and by talking to experts. The direct applications of math for such an engineer would be pretty slim to nonexistent.
- argonaut 10y agoThat isn't "doing machine learning," for the same reason that web developers aren't "operating systems programmers" (even though they use operating systems and need to know some OS concepts).