# What is the difference between machine learning approaches and Fourier series to fit a curve to data graph?

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?

## 1 Answer

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.