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Rather than giving a full response I would like add a factor in the distinction between the two. Let's make the example of a neural network used for classification, most of the times when people get the results they wanted they don't know exactly why they are getting those results. While statistics is more rigorous and always comes with a measure of the ...


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Just for the sake of argument, I am putting my two cents here. As I find the answers above/below so far are pretty explanatory. David DN rounded your question up nicely, I think. This subject is very new and therefore, take what you get and run with it. I worked with stats and I worked in research. I also worked on predictive research. Even the big ...


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Personally, I find it very hard to draw a line between the two, as there is clearly some overlapping. Machine Learning is a field that is based on classical statistics and USES statistic models heavily. Also, the mathematics behind Machine Learning can get extremely complicated, so I really would not use the mathematical argument as a discriminant. One ...


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In my view, MCMC/bootstrapping/permutation methods all fall under the category of computational techniques. They aren't tied down to a specific approach or way of thinking about a problem but rather an algorithmic approach to a class of problems. Techniques that involve resampling and iteration don't arise from a machine learning framework, they come out of ...


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As @whuber notes in a comment, you do need to deal first with what seem to be incorrect premises in your approach. Most important, if your samples aren't truly random then "you can't use any of these methods to make inferences," as he put it. Fix that first. In terms of mean versus median as a measure of central tendency, the choice is yours based on your ...


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