# Newbie: What is the difference between hypothesis class and models?

I am new to machine learning and I am confused with the terminology. Thus far, I used to view a hypothesis class as different instance of hypothesis function... Example: If we are talking about linear classification then different lines characterized by different weights would together form the hypothesis class.

Is my understanding correct or can a hypothesis class represent anything which could approximate the target function? For instance, can a linear or quadratic function that approximates the target function together form a single hypothesis class or both are from different hypothesis classes?

2. Hypothesis classes also don't have to consist of only simple functions. If you manage to search over all piecewise-$\tanh^2$ functions, then those functions are what your hypothesis class includes.