The teaching is organized as follows:

Learning objectives

The course aims to introduce students to statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed, focusing on the mathematical basis of the different state-of-the-art methodologies. Furthermore, it aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand the requirements for their correct use. Complementary laboratory sessions will illustrate the use of fundamental algorithms and industrial case studies in which the student will be able to learn to analyze real datasets using Python software. At the end of the course the student will have to demonstrate: i) to know the main phases of data analysis and data preparation, ii) to know how to use the main regression models, iii) to know how to develop pro-feature selection solutions, iv) know how to use regularization methods, e.g., ridge regression, LASSO, elastic net, least angle regression, and classification, v) to know unsupervised methods, vi) to know and be able to develop algorithms for dimensionality reduction, principal component analysis (PCA), K-means clustering, hierarchical clustering, and cross-validation.

Examination methods

To pass the exam, students must demonstrate:
- to have understood the principles underlying data analysis and the development of predictive models through statistical learning techniques,
- to be able to expose the methodologies of data preparation and statistical learning in a precise and organic way without digressions, - knowing how to apply the knowledge acquired to solve problems in various application areas (e.g., industrial, medical, environmental monitoring)