Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Mathematics - Enrollment from 2025/2026
Academic year:
I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
1° 2° Python programming language D Maurizio Boscaini (Coordinator)
1° 2° SageMath F Zsuzsanna Liptak (Coordinator)
1° 2° History of Modern Physics 2 D Francesca Monti (Coordinator)
1° 2° History and Didactics of Geology D Guido Gonzato (Coordinator)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
1° 2° Advanced topics in financial engineering D Luca Di Persio (Coordinator)
1° 2° C Programming Language D Sara Migliorini (Coordinator)
1° 2° C++ Programming Language D Federico Busato (Coordinator)
1° 2° LaTeX Language D Enrico Gregorio (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Axiomatic set theory for mathematical practice F Peter Michael Schuster (Coordinator)
1° 2° Corso Europrogettazione D Not yet assigned
1° 2° Corso online ARPM bootcamp F Not yet assigned
1° 2° ECMI modelling week F Not yet assigned
1° 2° ESA Summer of code in space (SOCIS) F Not yet assigned
1° 2° Google summer of code (GSOC) F Not yet assigned
1° 2° Higher Categories - Seminar course F Lidia Angeleri (Coordinator)

Teaching code

4S008279

Credits

6

Language

English en

The teaching is organized as follows:

PART I en

Credits

3

Period

I semestre

Academic staff

Paolo Dai Pra

PART II en

Credits

3

Period

I semestre

Academic staff

Alberto Castellini

Learning outcomes

The objective is to introduce students to statistical modelling and exploratory data analysis. The mathematical foundations of Statistical Learning (supervised and unsupervised learning, deep learning) are developed with emphasis on the underlying abstract mathematical framework, aiming to provide a rigorous, self-contained derivation and theoretical analysis of the main models currently used in applications. Complimentary laboratory sessions will illustrate the use of both the key algorithms and relevant case studies, mainly by using standard software environments such as R or Python.

Bibliography

Reference texts
Author Title Publishing house Year ISBN Notes
T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning. Data mining, inference, and prediction. (Edizione 2) Springer 2009