Statistical learning (2020/2021)

Course code
Alessandro Farinelli
Academic sector
Language of instruction
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Teoria 5 II semestre Alberto Castellini, Ancora Da Definire, Alessandro Farinelli

Go to lesson schedule

Laboratorio 1 II semestre Alessandro Farinelli

Go to lesson schedule

Learning outcomes

The course aims to introduce students to the statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed by placing the emphasis on the mathematical basis of the different state-of-the-art methodologies. It also aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand their requirements for correct use. 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 by means of Python software.

At the end of the course the student has to show to have acquired the following skills:
● knowledge of the main stages of: data analysis and preparation
● ability to use the main regression models
● ability to develop pro-feature selection solutions
● ability to use regularization methods, e.g., ridge regression, LASSO, elastic net, least angle regression, and classification
● knowledge of unsupervised methods
● know and know how to develop algorithms in the field of dimensionality reduction, analysis of the main components (PCA), K-means clustering, hierarchical clustering, and cross-validation