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

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 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 information on how to activate the internship. 

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

Modules and other activities that can be entered independently in the booklet

Semester 1 From 10/2/23 To 1/26/24
years Modules TAF Teacher
1° 2° Introduction to smart contract programming for ethereum D Sara Migliorini (Coordinator)
Semester 2 From 3/4/24 To 6/14/24
years Modules TAF Teacher
1° 2° Python programming language D Carlo Combi (Coordinator)
1° 2° Programming Challanges D Romeo Rizzi (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Cooperative Game Theory in the (Deep) RL Era D Alessandro Farinelli (Coordinator)

Teaching code

4S010686

Coordinator

Gloria Menegaz

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING

Period

Semester 1 dal Oct 2, 2023 al Jan 26, 2024.

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

Fundamentals of signal and image processing

Program

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 with particular focus on convolutional neural networks.
Syllabus
The course consists of three blocks: modeling of the Human Visual System (HVS), multiresolution signal representation and analysis of deep learning algorithms with focus Convolutional Neural Networks (CNNs).
Part 1: Human Visual System (HVS) – 10 hours
Introduction to Visual Intelligence
Foudations of vision, stimulus encoding, representation and interpretation
HVS modeling: multiscale processing of the visual stimuli, Contrast Sensitivity Function (CSF), color vision and perception, Color Matching Functions (CMFs)
High-level modeling of the HVS: structural and functional connectivity and graph-based modeling
Part 2: Multiresolution analysis – 20 hours
Background
Mathematical tools
Fourier transform in 1D and 2D
Windowed Fourier Transform
Wavelets and multiresolution representations
Wavelets Bases
Families of Wavelet Transforms (WT) and their properties
Fast Discrete Wavelet Tranforms (DWT)
WT in two dimensions
Scattering transform
Part 3: Application to the analysis and interpretation of deep convolutional neural networks (CNNs)– 10 hours
Overview on CNNs
The issue of interpretability, main approaches
CNN, HVS and multiresolution: getting to a unified view
CNN interpretation based on multiresolution theory and HVS models
Examples of interpretable DL
LABORATORY
Laboratory sessions will consist in Matlab and Python exercises on the topics covered in the theory lessons.

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

The lessons will be face to face. The recordings of previous years can be made available under request.

Learning assessment procedures

The exam will consist of a project and an interview on the topics covered in 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

To pass the exam, the student must demonstrate: - Understanding the fundamental theoretical aspects relating to the three parts of the course - Understanding the relationships between the topics covered and the differences and similarities at the modeling level - Having acquired skills theoretical and practical knowledge of multiresolution theory and its implications in the field of interpretability of deep machine learning models - Be able to transpose the skills acquired into solutions to concrete problems in a multidisciplinary environment.

Criteria for the composition of the final grade

The final grade will be determined by the outcome of the presentation of the project and the discussion

Exam language

Inglese