Machine learning is a computer science research area that deals with methods to identify and implement systems and algorithms by which a computer can learn, based on the examples given in the input. The challenge of machine learning is to allow a computer to learn how to automatically recognize complex patterns and make decisions that are as smart as possible. The entire learning process requires a dataset as follows:

• Training set: This is the knowledge base used to train the machine learning algorithm. During this phase, the parameters of the machine learning model (hyperparameters) can be tuned according to the performance obtained.
• Testing set: This is used only for evaluating the performance of the model on unseen data.

Learning theory uses mathematical tools that are derived from probability theory of and information theory. This allows you to assess the optimality of some methods over others.

There are basically three learning paradigms that will be briefly discussed:

• Supervised learning
• Unsupervised learning
• Learning with reinforcement

Let's take a look at them.