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 |
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Semester 1 | Oct 1, 2024 | Jan 31, 2025 |
Semester 2 | Mar 3, 2025 | Jun 13, 2025 |
Session | From | To |
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Winter exam session | Feb 3, 2025 | Feb 28, 2025 |
Summer exam session | Jun 16, 2025 | Jul 31, 2025 |
Autumn exam session | Sep 1, 2025 | Sep 30, 2025 |
Session | From | To |
---|---|---|
Sessione di laurea estiva | Jul 17, 2025 | Jul 17, 2025 |
Sessione di laurea autunnale | Oct 21, 2025 | Oct 21, 2025 |
Sessione di laurea invernale | Mar 27, 2026 | Mar 27, 2026 |
Period | From | To |
---|---|---|
Tutti i Santi | Nov 1, 2024 | Nov 1, 2024 |
Festa dell'Immacolata | Dec 8, 2024 | Dec 8, 2024 |
Vacanze di Natale | Dec 23, 2024 | Jan 6, 2025 |
Vacanze di Pasqua | Apr 18, 2025 | Apr 21, 2025 |
Festa della Liberazione | Apr 25, 2025 | Apr 25, 2025 |
Festa del Lavoro | May 1, 2025 | May 1, 2025 |
Festa del Santo Patrono | May 21, 2025 | May 21, 2025 |
Festa della Repubblica | Jun 2, 2025 | Jun 2, 2025 |
Vacanze estive | Aug 11, 2025 | Aug 16, 2025 |
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.
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 enrollment year.
1° Year
Modules | Credits | TAF | SSD |
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2° Year It will be activated in the A.Y. 2025/2026
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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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.
Machine learning (2024/2025)
Teaching code
4S011696
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Credits
4
Period
Semester 1
Academic staff
Cigdem Beyan
Laboratorio
Credits
2
Period
Semester 1
Academic staff
Cigdem Beyan
Learning objectives
The course aims to provide the theoretical foundations and describe the main methodologies relating to the area of machine learning, together with the most recent techniques of deep learning. In particular, the course will deal with describing the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are at the base, are used, and often complement many other disciplines and application areas of wide diffusion, such as computational vision, robotics, image processing, data mining, analysis and interpretation of medical and biological data, bioinformatics, biometrics, video surveillance, forecasting. More precisely, the methodologies that will be introduced in the course are often an integral part of the application areas mentioned above, and constitute the "intelligent" part of it with the final aim of understanding (classifying, recognizing, analyzing) the data coming from the process of interest (be they signals, images, strings, categorical, or other types). Starting from the type of data measured, the entire analysis pipeline will be considered, such as the extraction and selection of characteristics, supervised and unsupervised machine learning methods, parametric and non-parametric analysis techniques, and validation protocols, together with visualization necessary for understanding deep learning systems. At the laboratory level, real case studies and not just academic benchmarks will be presented, addressed with appropriate programming tools. In conclusion, the course aims to provide the student with a set of theoretical foundations and algorithmic tools to address the problems that may be encountered in strategic and innovative industrial sectors such as those involving the processing of large amounts of data (big data), multimedia, visual inspection of products, automation and forecasting.
Program
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UL: Foundation of Machine Learning - Theory
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The course is divided into two modules: Foundation of Machine Learning (ML) and Deep Learning (DL).
ML intends to provide the theoretical foundations and main methods relating to the analysis of data, not necessarily images. In short, theories and methods of statistical classification will be addressed. These topics are preparatory to the most recent Deep Learning techniques.
Addressed topics:
- Introduction: What is machine learning? Examples of Applications, main challenges of machine learning, tasks of machine learning, main ingredients
- Classification: binary classifier, performance measures (confusion matrix, precision, recall...), multi-class classification, multilabel classification, cross-validation
- Regression: linear regression, polynomial regression, logistic regression
- Bayesian Decision Theory and parameter estimation
- Nonparametric Methods: Histogram, Parzen windows, k-nearest neighbors
- Decision trees
- Ensemble learning and random forest
- Linear classifiers and discriminant functions: Perceptron, Relaxation, MSE, LMSE, gradient descent
- Linear transformations, Dimensionality reduction, Fisher transform. Principal Component Analysis, feature selection
- Kernel Methods and Support Vector Machines
- Unsupervised learning techniques: Clustering, gaussian mixture models
- Sequential data analysis: Markov models and hidden Markov models
- Machine learning versus deep learning
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UL: Foundation of Machine Learning - Laboratorio
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1) Introduction to Colab, Pytorch, Tensorflow
I/O data types, e.g., tabular data, images
2) Classification with scikitlearn: e.g., K-NN, evaluation
3) Data preparation, preprocessing, forward feature selection, data augmentation, normalization, missing data, one-hot vector
4) Principal component analysis & Fisher discriminant analysis
5) Clustering: K-means and elbow method, bag of words
6) Clustering methods and their comparisons, visualization methods (e.g., t-sne)
7) Support Vector Machines vs. Random forest
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UL: Deep Learning - Teoria
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The course is divided into two modules: Foundation of Machine Learning (ML) and Deep Learning (DL).
ML intends to provide the theoretical foundations and main methods relating to the analysis of data, not necessarily images. In short, theories and methods of statistical classification will be addressed. These topics are preparatory to the most recent Deep Learning techniques.
DL intends to provide theories and methods relating to the analysis of data (of various types, images, videos, text, sequences, etc.) using deep neural architectures, focusing on the structure and functioning of the different models such as, just as examples , convolutional networks, encoder-decoder models, attention models and transformer, and many others.
After an introduction of the importance of this area and its applications, the course includes topics such as artificial neural networks, convolutional networks, autoencoders - variational and non-variational, transformers, networks recurrent, generative models - adversarial and non-adversarial, multimodal models, methods for knowledge transfer and domain adaptation, etc.
The course will present the theoretical and methodological aspects, with associated application examples.
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UL: Deep Learning - Laboratorio
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The Lab classes are devoted to the development of algorithms in Python language of some of the models explained during the Theory classes.
Learning assessment procedures
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UL: Foundation of Machine Learning - Theory
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To pass the exam, students must demonstrate:
- Understanding of the principles underlying machine learning and methods for programming modules based on machine learning.
-Ability to articulate concepts of machine learning and programming of ML modules precisely and cohesively, without digressions.
-Application of acquired knowledge to solve practical problems presented through exercises, questions, and projects.
-The exam involves a project that can be performed individually or in pairs. The oral exam will cover questions related to the project, theoretical concepts, and lab exercises.
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UL: Foundation of Machine Learning - Laboratorio
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The exam includes a project that can be done individually or in pairs. The oral exam will cover project-related questions and laboratory exercises in accordance with the rules of the machine learning theory course.
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UL: Deep Learning - Teoria
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The exam will consist in the development of a project (2 people max, 3 people inexceptional cases, to be agreed with the teachers), followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked about the theoretical part of the course described in class, aimed at assessing the knowledge of the contents of the course.
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UL: Deep Learning - Laboratorio
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The exam will consist in the development of a project (2 people max, 3 people inexceptional cases, to be agreed with the teachers), followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked about the theoretical part of the course described in class, aimed at assessing the knowledge of the contents of the course.
Type D and Type F activities
Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.
1. Insegnamenti impartiti presso l'Università di Verona
Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).
Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.
2. Attestato o equipollenza linguistica CLA
Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:
- Lingua inglese: vengono riconosciuti 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
- Altre lingue e italiano per stranieri: vengono riconosciuti 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).
Tali cfu saranno riconosciuti, fino ad un massimo di 6 cfu complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D. Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.
Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.
Modalità di inserimento a libretto: richiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it
3. Competenze trasversali
Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali
Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.
4. Contamination lab
Il Contamination Lab Verona (CLab Verona) è un percorso esperienziale con moduli dedicati all'innovazione e alla cultura d'impresa che offre la possibilità di lavorare in team con studenti e studentesse di tutti i corsi di studio per risolvere sfide lanciate da aziende ed enti. Il percorso permette di ricevere 6 CFU in ambito D o F. Scopri le sfide: https://www.univr.it/clabverona
ATTENZIONE: Per essere ammessi a sostenere una qualsiasi attività didattica, incluse quelle a scelta, è necessario essere iscritti all'anno di corso in cui essa viene offerta. Si raccomanda, pertanto, ai laureandi delle sessioni di dicembre e aprile di NON svolgere attività extracurriculari del nuovo anno accademico, cui loro non risultano iscritti, essendo tali sessioni di laurea con validità riferita all'anno accademico precedente. Quindi, per attività svolte in un anno accademico cui non si è iscritti, non si potrà dar luogo a riconoscimento di CFU.
5. Periodo di stage/tirocinio
Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico) qui sono riportate le informazioni su come attivare lo stage.
Verificare nel regolamento quali attività possono essere di tipologia D e quali di tipologia F.
Si ricorda, inoltre, che per i tirocini attivati dal 1 ottobre 2024 sarà possibile riconoscere le ore eccedenti in termini di crediti di tipologia D limitatamente alle sole esperienze di tirocinio svolte presso enti ospitanti esterni all’Ateneo.
Insegnamenti e altre attività che si possono inserire autonomamente a libretto valide per l'a.a. 2024/25
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
1° 2° | Elements of Cosmology and General Relativity | D |
Claudia Daffara
(Coordinator)
|
1° 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
1° 2° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
1° 2° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
1° 2° | Python programming language | D |
Carlo Combi
(Coordinator)
|
1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Graduation
Deadlines and administrative fulfilments
For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.
Need to activate a thesis internship
For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.
Final examination regulations
List of thesis proposals
Attendance modes and venues
As stated in the Teaching Regulations, attendance at the course of study is not mandatory.
Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.
The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus.
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.