When it comes to supervised learning, there are two families of learning algorithms: parametric and non-parametric. This area also happens to be a hotbed for gatekeeping and opinion-based conjecture regarding which is better. Basically, parametric models are finite-dimensional, which means that they can learn only a defined number of model parameters. Their learning stage is typically categorized by learning some vector theta, which is also called a coefficient. Finally, the learning function is often a known form, which we will clarify later in this section.
Parametric models
Finite-dimensional models
If we go back to our definition of supervised learning, recall that we need to learn some function, f. A parametric...
