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 Artificial Intelligence - Enrollment from 2025/2026

Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.

1. Modules taught at the University of Verona

Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).

Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.

2. CLA certificate or language equivalency

In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:

  • English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
  • Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).

These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.

Those enrolled until A.Y. 2020/2021 should consult the information found here.

Method of inclusion in the bookletrequest the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it

3. Transversal skills

Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali

Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.  

4. Contamination lab

The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.  

Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).  

Find out more:  https://www.univr.it/clabverona 

PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.  

5. Internship/internship period

In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulationshere you can find information on how to activate the internship. 

Check in the regulations which activities can be Type D and which can be Type F.

Please also note that for traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits limited only to traineeship experiences carried out at host organisations outside the University.

Academic year:

Teaching code

4S010673

Credits

12

Coordinator

Vittorio Murino

Language

English en

Also offered in courses:

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Courses Single

Authorized

The teaching is organized as follows:

Foundation of Machine Learning - Teoria

Credits

4

Period

Semester 1

Academic staff

Cigdem Beyan

Foundation of Machine Learning - Laboratorio

Credits

2

Period

Semester 1

Academic staff

Cigdem Beyan

Deep Learning - Teoria

Credits

4

Period

Semester 2

Deep Learning - Laboratorio

Credits

2

Period

Semester 2

Academic staff

Vittorio Murino

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
------------------------
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.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

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UL: Foundation of Machine Learning - Theory
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Frontal lessons in the classroom and in computer classrooms for lab lectures
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UL: Foundation of Machine Learning - Laboratorio
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Laboratory experiences, exercises.
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UL: Deep Learning - Teoria
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Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be on the computer in the computer room and will consist in the development of some of the algorithms developed in class.
The Laboratory lessons will be aimed at developing practical examples of some of the topics described in the Theory part of the course. The lessons will take place in a computer laboratory in Phyton language.
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UL: Deep Learning - Laboratorio
------------------------
Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be on the computer in the computer room and will consist in the development of some of the algorithms developed in class.
The Laboratory lessons will be aimed at developing practical examples of some of the topics described in the Theory part of the course. The lessons will take place in a computer laboratory in Phyton language.

Learning assessment procedures

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UL: Foundation of Machine Learning - Theory
------------------------
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
------------------------
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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

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UL: Foundation of Machine Learning - Theory
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Theoretical and applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques taught in the course.
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UL: Foundation of Machine Learning - Laboratorio
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Applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques learned during the laboratory.
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UL: Deep Learning - Teoria
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To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning, Deep Learning & Artificial Intelligence techniques described in class;
- be able to present one's arguments in a precise, organic and structured way, without digressions;
- knowing how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.
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UL: Deep Learning - Laboratorio
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To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning, Deep Learning & Artificial Intelligence techniques described in class;
- be able to present one's arguments in a precise, organic and structured way, without digressions;
- knowing how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.

Exam language

------------------------ UL: Foundation of Machine Learning - Teoria ------------------------ Inglese ------------------------ UL: Foundation of Machine Learning - Laboratorio ------------------------ Inglese ------------------------ UL: Deep Learning - Teoria ------------------------ Inglese ------------------------ UL: Deep Learning - Laboratorio ------------------------ Inglese