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