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As I know machine learning(at least in some problems) tries to fit a curve to data graph. And I think Fourier transform tried to do it. But machine learning use a hypothesis curve with the formula like h = w1x1 + w2x2 + ... + wnxn but Fourier uses a combination of Sin()/Cos() functions.

What is the difference? Why machine learning doesn't use a Sin()/Cos() combination like Fourier series/transforms?

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Using Fourier series is machine learning. Machine/Statistical learning is just a general term for all kinds of tasks where learning from data is required.

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