Learning Paradigms

In this section, we will discuss the similarities and differences between the three main learning paradigms under the umbrella of machine learning. We will analyze some representative problems in order to understand the characteristics of these frameworks better.

Introduction to Learning Paradigms

For a learning paradigm, we implement a problem and a solution method. Usually, learning paradigms deal with data and rephrase the problem in a way that can be solved by finding parameters and maximizing an objective function. In these settings, the problem can be faced using mathematical and optimization tools, allowing a formal study. The term "learning" is often used to represent a dynamic process of adapting the algorithm's parameters in such a way as to optimize their performance (that is, to learn) on a given task. Tom Mitchell defined learning in a precise way, as follows:

"A computer program is said to learn from experience E with respect...