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> he criticizes all of machine learning as simply unprincipled revival of nonparametric curve estimation He's right. 'Machine learning' is just statistics with

> he criticizes all of machine learning as simply unprincipled revival of nonparametric curve estimation He's right. 'Machine learning' is just statistics with better, cleaner names for things. The underlying theory is the same. (The commenter below says that 'statistics explains, ML predicts', but this isn't really true. Both statistics and ML build models, whether you use these models for prediction or explanation is up to you.) One problem is that the 'statistics' as usually taught in a college course or explained in a textbook comes from a much earlier age, before supercomputers were available to the average man; in essence, it's like a kind of machine learning as done by paper-and-pencil. In contrast, 'ML' assumes from the start that computing resources will be available.

msellout 11y ago
> In contrast, 'ML' assumes from the start that computing resources will be available. Right, but I'm not sure that "better, cleaner names for things" actually follows. Instead, I find that the ML folks just hacked their way to similar results as traditional statistics, but in many cases were comfortable with the algorithms as "black boxes" rather than having a clear understanding of why the algorithms worked. In that sense, the author's "unprincipled" criticism is valid. This is less true today, but the new research in convolutional neural nets shows how ML starts from hacking things until they produce practical results then backing into the theory of why. This habit has resulted in much duplication of effort and naming schemes. My ML prof at Georgia Tech (Isbell was awesome! http://www.cc.gatech.edu/~isbell/ http://www.cc.gatech.edu/~isbell/) constantly trashed "genetic algorithms" for being a silly form of randomized hill-climbing. The beneficial side of these less-principled techniques is that they happen to work on larger scale datasets. It turns out approximate results are more scalable than exact results.

msellout 11y ago

> In contrast, 'ML' assumes from the start that computing resources will be available. Right, but I'm not sure that "better, cleaner names for things" actually follows. Instead, I find that the ML folks just hacked their way to similar results as traditional statistics, but in many cases were comfortable with the algorithms as "black boxes" rather than having a clear understanding of why the algorithms worked. In that sense, the author's "unprincipled" criticism is valid. This is less true today, but the new research in convolutional neural nets shows how ML starts from hacking things until they produce practical results then backing into the theory of why. This habit has resulted in much duplication of effort and naming schemes. My ML prof at Georgia Tech (Isbell was awesome! http://www.cc.gatech.edu/~isbell/ http://www.cc.gatech.edu/~isbell/) constantly trashed "genetic algorithms" for being a silly form of randomized hill-climbing. The beneficial side of these less-principled techniques is that they happen to work on larger scale datasets. It turns out approximate results are more scalable than exact results.