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.
Type D and Type F activities
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| 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)
|
| 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)
|
| 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)
|
Statistical learning (2019/2020)
The teaching is organized as follows:
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
| 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 |
