Yes, nonparametric methods make fewer asumptions and indeed there are many techniques imported from statistics into learning, including nonparametric methods. In learning the focus is more on classification, and hence there are several new concepts that one would not encounter in a traditional statistics setting, such as the VCdimension.
The way I would look at it is that what we want to do is learn from data. How we do it may differ from discipline to discipline. The traditional statistician typically looks for some rigorous mathematical model within which to deduce interesting statements. The machine learner uses computationally tractable algorithms to output a hypothesis and then asks what can one say about the performance of that final hypothesis.
It is perfectly valid to be both a machine learner and a statistician; using their broadest definitions, some might argue that ML subsumes statistics, and others would say statistics subsumes ML. To me ML and statistics are how you do things, and there is indeed big overlap, but what we do stays the same: learning from data.
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Originally Posted by scottedwards2000
True that statistics often makes distributional assumptions. But what about the field of nonparametric statistics, which makes little to no assumptions about distributions? Many interesting techniques there like resampling and the jackknife.
