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 2025/2026Type 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 booklet: request 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 Regulations) here 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.
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 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° | Python programming language [English edition] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Mini-course on Deep Learning & Medical Imaging | D |
Vittorio Murino
(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° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Python programming language [Edizione in italiano] | 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)
|
Visual Intelligence (2024/2025)
Teaching code
4S010686
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
The course aims at providing competence about analysis, modeling and interpretation of multidimensional signals and images with focus on artificial vision and machine learning aspects, targeting applications in the field of multimedia and interpretable machine learning. At the end of the course the students will be able to autonomously solve typical problems requiring multidimensional signal modeling, feature extraction, analysis and interpretation of the outcomes of machine learning algorithms in the field of multimedia and artificial vision.
Examination methods
To pass the exam, students must demonstrate:
- to have understood the principles underlying visual intelligence
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Prerequisites and basic notions
None
Program
The course consists of two blocks: 1) signal representation using multiresolution analysis and 2) eXplainable AI methods with particular emphasis on deep learning and convolutional networks (CNN). Both blocks include a theory part and a laboratory part, which are integrated at the end of the course to form the basis for the exam project.
Part 1: Multiresolution analysis – 12 hours theory, 8 hours lab
- Revisiting the Fourier transform in 1D and 2D
- Windowed Fourier Transform
- Wavelets and multiresolution representations
- Wavelet bases Families of multiscale transforms and their properties
- Fast implementation of the transform discrete wavelet (DWT)
- Discrete wavelet transform in 2D
- Scattering transform
Part 2: eXplainabe AI – 12 hours theory, 16 hours lab
Interpretable models (White box)
- Linear regression, logistic regression, GLM, decision trees….
- Global model agnostic methods
Model Agnostic Local Methods
- Local Surrogate Model (LIME), Shapley Adaptive Explanations (SHAP)
Focus on Deep Neural Networks
- Deep learning overview
- Visualization methods (layers, filters, activation maps)
- Pixel attribution values and saliency maps
- Gradient-based methods (integrated gradients and variants)
- Validation of XAI results
Lessons will be complemented by practical hands-on sessions.
Bibliography
Didactic methods
Lessons will be delivered in person and in streaming. Lessons will be recorded and made available.
Learning assessment procedures
The exam involves the development and discussion of a project related to the topics covered in the course. The exam consists in the presentation of the project through slides and the discussion of the related theoretical and applicative aspects in the form of an oral interview.
Evaluation criteria
To pass the exam the student must demonstrate: - Having understood the fundamental theoretical aspects relating to the two parts into which the teaching is divided - Having understood the relationships between the topics covered - Having acquired theoretical and practical skills relating to the theory of multiresolution and its implications in the field of interpretability of deep machine learning models - Being able to transpose the skills acquired into solutions to concrete problems in a multidisciplinary field.
Criteria for the composition of the final grade
The final grade will be determined by the outcome of the oral exam.
Exam language
Inglese
