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.
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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| I semestre | Oct 1, 2020 | Jan 29, 2021 |
| II semestre | Mar 1, 2021 | Jun 11, 2021 |
| Session | From | To |
|---|---|---|
| Sessione invernale d'esame | Feb 1, 2021 | Feb 26, 2021 |
| Sessione estiva d'esame | Jun 14, 2021 | Jul 30, 2021 |
| Sessione autunnale d'esame | Sep 1, 2021 | Sep 30, 2021 |
| Session | From | To |
|---|---|---|
| Sessione di laurea estiva | Jul 22, 2021 | Jul 22, 2021 |
| Sessione di laurea autunnale | Oct 14, 2021 | Oct 14, 2021 |
| Sessione di laurea invernale | Mar 16, 2022 | Mar 16, 2022 |
| Period | From | To |
|---|---|---|
| Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
| Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
| Vacanze Pasquali | Apr 2, 2021 | Apr 5, 2021 |
| Festa del Santo Patrono | May 21, 2021 | May 21, 2021 |
| Festa della Repubblica | Jun 2, 2021 | Jun 2, 2021 |
| Vacanze estive | Aug 9, 2021 | Aug 15, 2021 |
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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
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.
| Modules | Credits | TAF | SSD |
|---|
1° Year
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
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.
Statistical learning - PART I (2020/2021)
Teaching code
4S008279
Teacher
Credits
3
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Language
English
Period
I semestre dal Oct 1, 2020 al Jan 29, 2021.
To show the organization of the course that includes this module, follow this link: Course organization
Learning outcomes
The objective is to introduce 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.
Program
The entire course will be available online. In addition, a number of the lessons/all the lessons (see the course
schedule) will be held in-class.
1. Linear regression
Normal random vectors and their properties. Linear regression models. Least squares and
projections. Parameter estimators and their optimality (Gauss-Markov Theorem, with
proof). Distribution of the estimators. Testing predictors’ significance. Best subset selection
and its formulation as Mixed Integer Optimization problem. Ridge regression.
Interpretation of ridge regression with the singular value decomposition (with proof).
LASSO.
2. Linear methods for classification
Bayes classifier and its optimality (with proof). Linear regression after binary coding. Linear
discriminant analysis. Separating hyperplanes. The perceptron algorithm (with proof of
termination)
3. Model selection and assessment.
Loss function; training and prediction error. Cross validation. Explicit expression of cross
validation for linear regression (with proof). Bootstrap and application to model
assessment.
4. Clustering.
Center based clustering. K-center clustering; K-median clustering; K-means clustering.
Lloyd’s algorithm for K-means. Ward’s algorithm. Spectral clustering: graph Laplacian. The
multiplicity of the eigenvalue 0 of the graph Laplacian equals the number of connected
components (with proof). Unnormalized and normalized spectral clustering algorithms.
Relation of spectral clustering with graph-cut.
5. Introduction to Neural Networks.
Single layer neural networks. Cybenko’s density theorem (with proof). Multilayer neural
networks. Training a neural network: the gradient descent algorithm.
Bibliografia
| 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 |
Examination Methods
Oral exam
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Algorithms | D |
Roberto Segala
(Coordinatore)
|
| 1° 2° | Scientific knowledge and active learning strategies | F |
Francesca Monti
(Coordinatore)
|
| 1° 2° | Genetics | D |
Massimo Delledonne
(Coordinatore)
|
| 1° 2° | History and Didactics of Geology | D |
Guido Gonzato
(Coordinatore)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Advanced topics in financial engineering | F |
Luca Di Persio
(Coordinatore)
|
| 1° 2° | Algorithms | D |
Roberto Segala
(Coordinatore)
|
| 1° 2° | Python programming language | D |
Vittoria Cozza
(Coordinatore)
|
| 1° 2° | Organization Studies | D |
Giuseppe Favretto
(Coordinatore)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 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° | Introduzione all'analisi non standard | F |
Sisto Baldo
|
| 1° 2° | C Programming Language | D |
Pietro Sala
(Coordinatore)
|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinatore)
|
| 1° 2° | Mathematics mini courses | F |
Marco Caliari
(Coordinatore)
|
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.
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.
Area riservata studenti
Abbreviazione di carriera
A breve verranno inserite le informazioni per l'A.A. 2022/2023.
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
| Title | Info File |
|---|---|
1. Convenzione | Learning Agreement UNITN - UNIVR
|
167 KB, 27/08/21 |
|
2. Sostituzione insegnamenti a UNITN - Courses replacement at UNITN
|
44 KB, 30/08/21 |
|
3. Sostituzione insegnamenti a UNIVR - Courses replacement at UNIVR
|
113 KB, 30/08/21 |
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!
Gestione carriere
Graduation
Attachments
| Title | Info File |
|---|---|
|
1. Come scrivere una tesi
|
31 KB, 29/07/21 |
|
2. How to write a thesis
|
31 KB, 29/07/21 |
|
4. Regolamento tesi (valido da luglio 2020)
|
259 KB, 29/07/21 |
|
5. Regolamento tesi (valido da luglio 2022)
|
256 KB, 29/07/21 |
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 |
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.