In this tutorial I will illustrate the main approaches for learning temporal logic models from data. In the first part of the tutorial, I will illustrate the main Machine Learning techniques that induce propositional logic models, such as decision trees and rules. Then I will discuss the field of Inductive Logic Programming that offers techniques for inducing first-order logic models. I will also briefly touch on the extension of Inductive Logic Programming to Probabilistic Inductive Logic Programming. In the second part of the tutorial, I will present approaches for dealing with the temporal dimension in Machine Learning, Inductive Logic Programming and Probabilistic INductive Logic Programming, including learning techniques for specific temporal logics.