Academic staff

Claudia Di Caterina, Lorenzo Frattarolo

Referent

Claudia Di Caterina

Credits

5

Language

English

Class attendance

Free Choice

Location

VERONA

Moodle Seminars 0

Learning objectives

Introduce students to the theory of statistical learning through models and examples.

Prerequisites and basic notions

Knowledge of linear algebra and probability is assumed.

Program

Part 1 - Lecturer: Claudia Di Caterina.
Linear Methods for Regression: OLS, Gauss-Markov, variable selection, Lasso and shrinkage.
Part 2 - Lecturer: Lorenzo Frattarolo.
Basis Expansions and Smoothing Methods: regularization, reproducing kernel Hilbert spaces, wavelet smoothing, local regression, kernel density estimation. Introduction to Machine Learning: neural networks and random forests.

Bibliography

Didactic methods

Frontal teaching.

Learning assessment procedures

There is both the possibility of taking a written exam test in classical form with questions related to topics covered in lectures and the possibility of writing a report on findings from the literature on statistical learning.

Assessment

Methodological rigor, critical analysis and clarity of exposition.

Criteria for the composition of the final grade

The final evaluation will be based on the grade of the written exam test or of the report.

Scheduled Lessons

Sustainable Development Goals - SDGs

This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability