Università Roma Tre

The course aims to explore the fundamental concepts of statistical learning, introducing a wide range of tools and techniques such as linear and nonlinear models, decision trees, neural networks, and dimensionality reduction methods. The main objective is to provide both theoretical knowledge and practical skills in data manipulation and analysis, as well as in the evaluation and interpretation of model results with particular attention to supervised learning problems. Real-world problems will be addressed to understand the theoretical principles underlying learning algorithms and to learn their effective application using specific libraries of the statistical software R.