The course aims to introduce the fundamental principles of statistical learning for data analysis, with particular attention to regression and classification problems in supervised settings. By the end of the course, students will be able to understand the main statistical models for predictive analysis; evaluate and select models using statistical criteria and validation methods; interpret model results and assess their performance; use R (and RStudio) software to implement basic statistical learning analyses. The course emphasizes an applied approach, with practical examples based on real data.