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I would consider machine learning to be an application of statistics, in much the same way that mechanical engineering is an application of physics. The foundat

I would consider machine learning to be an application of statistics, in much the same way that mechanical engineering is an application of physics. The foundations of mechanical engineering are rooted in physical concepts, but mechanical engineers have formulas for all sorts of things, like calculating the fatigue life of gears, that you can't get from pure physics because they are empirically derived curve fits, rather than natural laws. The science is more pure, but the application of it is what's powerful in the real world. So in a sense I agree that "machine learning is not statistics," but I strongly disagree with the tone of the article, which is "we're better than statistics." Don't shit on the shoulders you're standing on.

tabtab 8y ago
It may not be an "application of statistics" in the direct sense, but that depends on how one defines statistics, which brings one back to the original problem. Statistics and ML often have similar goals, but ML emphasizes computational efficiency over trace-able accuracy. Thus, I view each field as having different weights on the same sub-goals.
RhysU 8y ago
>...you can't get from pure physics... You could use natural laws, if you want to propagate a mess of uncertain parameters through a complicated forward problem. Interpolation within empirical data is wildly easier, but a masochist could do gear fatigue ab initio in silico.