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 |
| 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
Bullini Orlandi Ludovico
ludovico.bulliniorlandi@univr.it 045 802 8095
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 |
|---|
2° 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 (2020/2021)
Teaching code
4S009067
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Teoria
Credits
5
Period
II semestre
Academic staff
Laboratorio
Learning outcomes
The course aims to introduce students to the statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed by placing the emphasis on the mathematical basis of the different state-of-the-art methodologies. It also aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand their requirements for correct use. Laboratory sessions will illustrate the use of fundamental algorithms and industrial case studies in which the student will be able to learn to analyze real datasets by means of Python software.
At the end of the course the student has to show to have acquired the following skills:
● knowledge of the main stages of: data analysis and preparation
● ability to use the main regression models
● ability to develop pro-feature selection solutions
● ability to use regularization methods, e.g., ridge regression, LASSO, elastic net, least angle regression, and classification
● knowledge of unsupervised methods
● know and know how to develop algorithms in the field of dimensionality reduction, analysis of the main components (PCA), K-means clustering, hierarchical clustering, and cross-validation
Program
-- Linear models for Regression (Linear Regression, Subset Variable Selection, Shrinkage/Regularization)
-- Classification models (Logistic Regression, Linear Discriminant Analysis)
-- Tree Based Methods (Decision Trees, Bagging, Random Forest, Boosting)
-- Unsupervised methods (Principal Component Analysis, K-Means Clustering, Hierarchical Clustering)
-- Model Assessment and selection (cross validation)
-- Introduction to Neural Networks (Single layer neural network, training a neural network)
Lab:
-- Linear regression and related variable selection/shrinkage methods (in Python)
-- Classification with logistic regression (in Python)
-- Clustering with k-means and hierarchical clustering (in Python)
-- Artificial Neural Networks (in Python)
Examination Methods
The exam is composed of an oral test and the realization of a project that focuses on the application of statistical learning approaches to a specific case study.
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Nicola Fausto Spoto
(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.
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.
Graduation
Attachments
| Title | Info File |
|---|---|
Regolamento esame finale | Final exam regulation
|
387 KB, 27/04/22 |
List of theses and work experience proposals
| theses proposals | Research area |
|---|---|
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |
| Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
| Domain Adaptation | Computing methodologies - Machine learning |
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.
