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What is the difference between parameter estimation which includes system identification and learning in machine learning perspective?

Let say the model is y= Ax. x is the input and y is the output. In estimation, I have seen parameters to be estimated, maybe in this case it is A and the samples are also estimated (unsure)

What is learning then?

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    $\begingroup$ Nothing. Same idea, different fields. $\endgroup$ – Zoë Clark Jul 30 '14 at 1:45
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Basicaly same, but flavor of terms is a little different - by estimation people usually mean that you specify underlying distributions and then estimate their parameters. Learning may be distribution free - just optimizing some target function and it applies in situations with complex structured data when it not reasonable/possible to build a distributional model.

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