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

A.A. 2026/2027 A.A. 2025/2026 A.A. 2024/2025

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.

It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
12
C
INF/01
6
B
ING-INF/05
6
C
INF/01
1 module among the following
6
C
ING-IND/13
6
C
ING-INF/05
6
C
ING-INF/05
6
C
FIS/01
6
C
ING-INF/04
6
B
ING-INF/05
6
B
ING-INF/04
Final exam
3
E
-
Modules Credits TAF SSD
Between the years: 2°- 3°
6
F
-
Between the years: 2°- 3°
12
D
-
Between the years: 1°- 2°- 3°
3
E
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Artificial intelligence  (2026/2027)

Teaching code

4S012387

Teacher

Marco Cristani

Credits

6

Also offered in courses:

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

II semestre dal Mar 1, 2027 al Jun 11, 2027.

Courses Single

Authorized

Lessons timetable Seminars 0

Learning objectives

The course aims to provide the fundamental theoretical and technical knowledge of artificial intelligence underlying machine learning and decision theory, for classification and detection, computational vision and robotics applications. At the end of the course the student must demonstrate that they have:
knowledge and understanding relating to the problems of data acquisition, feature extraction, supervised and unsupervised training, testing of an intelligent system. In particular, they will have understood: the concept of gradient and minimization of a functional through iterative strategies, the concept of patterns, the concept of generalization;
The student will have understood the basics of a programming language for artificial intelligence such as Python;
ability to apply the knowledge acquired to program a simple artificial intelligence system;
ability to independently evaluate the advantages and disadvantages of different design choices in the field of artificial intelligence systems;
to be able to evaluate the environmental impact of certain artificial intelligence algorithms
The student must also demonstrate that they have the necessary skills to continue their studies independently in the field of artificial intelligence

Learning assessment procedures

Written test on all teaching topics, through open questions and exercises, for a duration of 3 hours. The exam method is the same for attending and non-attending students. There are no intermediate tests.

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 students must show that:
- they have understood the concepts related to the theory Artificial Intelligence;
- they are able to describe the concepts in a clear and exhaustive way;
- they are able to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.
The written exam will be evaluated with at most 33 points (30 cum Laude).