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.

A.A. 2018/2019

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2018 Jan 31, 2019
II semestre Mar 4, 2019 Jun 14, 2019
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2019 Feb 28, 2019
Sessione estiva d'esame Jun 17, 2019 Jul 31, 2019
Sessione autunnale d'esame Sep 2, 2019 Sep 30, 2019
Degree sessions
Session From To
Sessione di laurea estiva Jul 22, 2019 Jul 22, 2019
Sessione di laurea autunnale Oct 15, 2019 Oct 15, 2019
Sessione di laurea invernale Mar 19, 2020 Mar 19, 2020
Holidays
Period From To
Sospensione attività didattica Nov 2, 2018 Nov 3, 2018
Vacanze di Natale Dec 24, 2018 Jan 6, 2019
Vacanze di Pasqua Apr 19, 2019 Apr 28, 2019
Vacanze estive Aug 5, 2019 Aug 18, 2019

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D G L M O R S

Albi Giacomo

giacomo.albi@univr.it +39 045 802 7913

Angeleri Lidia

lidia.angeleri@univr.it 045 802 7911

Baldo Sisto

sisto.baldo@univr.it 045 802 7935

Bos Leonard Peter

leonardpeter.bos@univr.it +39 045 802 7987

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Caliari Marco

marco.caliari@univr.it +39 045 802 7904

Castellini Alberto

alberto.castellini@univr.it +39 045 802 7908

Daldosso Nicola

nicola.daldosso@univr.it +39 045 8027076 - 7828 (laboratorio)

Di Persio Luca

luca.dipersio@univr.it +39 045 802 7968

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Mantese Francesca

francesca.mantese@univr.it +39 045 802 7978

Marigonda Antonio

antonio.marigonda@univr.it +39 045 802 7809

Mazzuoccolo Giuseppe

giuseppe.mazzuoccolo@univr.it +39 0458027838

Migliorini Sara

sara.migliorini@univr.it +39 045 802 7908

Monti Francesca

francesca.monti@univr.it 045 802 7910

Orlandi Giandomenico

giandomenico.orlandi at univr.it 045 802 7986

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Sansonetto Nicola

nicola.sansonetto@univr.it 049-8027932

Schuster Peter Michael

peter.schuster@univr.it +39 045 802 7029

Solitro Ugo

ugo.solitro@univr.it +39 045 802 7977

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

CURRICULUM TIPO:
ModulesCreditsTAFSSD
6
B
(MAT/05)
Final exam
32
E
-

2° Year

ModulesCreditsTAFSSD
6
B
(MAT/05)
Final exam
32
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°To be chosen between
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
4
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S007624

Credits

6

Coordinatore

Alberto Castellini

Scientific Disciplinary Sector (SSD)

MAT/08 - NUMERICAL ANALYSIS

Language

English

The teaching is organized as follows:

Machine learning

Credits

3

Period

I semestre

Academic staff

Alberto Castellini

Statistical modelling

Credits

3

Period

I semestre

Academic staff

Leonard Peter Bos

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.

Program

- Introduction to data analysis with R and Python

- Linear methods for regression (linear regression, least squares, MLE: Estimation, Prediction, Tests under Gaussian assumptions, variable/subset selection

- Shrinkage/Regularization methods (Ridge regression, Least absolute shrinkage and selection operator, [Elastic net, Least angle regression])

- Linear methods for classification (Logistic regression, MLE: estimation, prediction, variable selection)

- Linear model assessment and selection (cross-validation, bootstrap methods)

- Clustering analysis (k-means, principal component analysis and spectral clustering)

Examination Methods

The purpose of the exam is to evaluate the capabilities of the student to understand and use the methodologies presented in the course. The exam consists of a project assignment about specific case studies. Alternatively, the student may choose to give a public presentation about advanced methodologies from the literature related to the topics of the course.

Bibliografia

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

Type D and Type F activities

Modules not yet included

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Gestione carriere


Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, except for specific practical or lab activities, attendance is not mandatory. Regarding these activities, please see the web page of each module for information on the number of hours that must be attended on-site.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Graduation

Attachments

List of theses and work experience proposals

theses proposals Research area
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Hamilton-Jacobi theories, including dynamic programming
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Manifolds
Controllo di sistemi multiagente Calculus of variations and optimal control; optimization - Optimality conditions
Formule di rappresentazione per gradienti generalizzati Mathematics - Analysis
Formule di rappresentazione per gradienti generalizzati Mathematics - Mathematics
Mathematics Bachelor and Master thesis titles Various topics
Stage Research area
Internship proposals for students in mathematics Various topics

Alternative learning activities

In order to make the study path more flexible, it is possible to request the substitution of some modules with others of the same course of study in Mathematics at the University of Verona (if the educational objectives of the modules to be substituted have already been achieved in the previous career), or with others of the course of study in Mathematics at the University of Trento.

Attachments


Double degree

The University of Verona, through a network of agreements with foreign universities, offers international courses that enable students to gain a Double/Joint degree at the time of graduation. Indeed, students enrolled in a Double/Joint degree programme will be able to obtain both the degree of the University of Verona and the degree issued by the Partner University abroad - where they are expected to attend part of the programme -, in the time it normally takes to gain a common Master’s degree. The institutions concerned shall ensure that both degrees are recognised in the two countries.

Places on these programmes are limited, and admissions and any applicable grants are subject to applicants being selected in a specific Call for applications.

The latest Call for applications for Double/Joint Degrees at the University of Verona is available now!


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.