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.

Study Plan

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

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.

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
Final exam
18
E
-
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
Final exam
18
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following
6
C
INF/01
Between the years: 1°- 2°
2 modules among the following
6
B
INF/01
Between the years: 1°- 2°
2 modules among the following
6
C
ING-INF/05
6
C
INF/01 ,ING-INF/05
6
C
INF/01
Between the years: 1°- 2°
Further activities: 3 cfu training and 3 cfu further language skill or 6 cfu training. Foreign students must acquire compulsory 3 credits of Italian language skills
6
F
-
Between the years: 1°- 2°

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S010686

Credits

6

Scientific Disciplinary Sector (SSD)

ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA

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.

Educational offer 2024/2025

ATTENTION: The details of the course (teacher, program, exam methods, etc.) will be published in the academic year in which it will be activated.
You can see the information sheet of this course delivered in a past academic year by clicking on one of the links below: