In this introductory lecture, I will present an overview of established machine-learning approaches and will focus on i) "exploratory analysis" performed by means of "unsupervised learning" methods and ii) methods for regularized regression, starting from linear regression, up to modern kernel regression methods. I will also discuss symbolic regression. In the hands-on session, participants will be guided through some of the presented methods by means of Jupyther notebooks run on NOMAD servers (no need for install dedicated software).