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

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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
9
A
MAT/03
6
A
MAT/05
12
C
BIO/09 ,BIO/16
9
B
ING-INF/05
6
A
CHIM/07
12
A
INF/01
6
A
FIS/01

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

ModulesCreditsTAFSSD
12
A
MAT/05 ,MAT/07
9
B
ING-IND/13
6
A
FIS/01
12
B
ING-INF/01
9
B
ING-INF/06
6
C
ING-IND/22

3° Year   activated in the A.Y. 2024/2025

ModulesCreditsTAFSSD
6
C
M-FIL/03 ,M-PSI/08
6
B
ING-INF/05
6
B
ING-INF/04
6
B
ING-IND/34
6
C
ING-INF/04
1 module among the following
6
C
ING-INF/05
6
C
ING-INF/06
6
C
ING-IND/06
6
C
ING-INF/05
6
C
ING-INF/06
6
C
ING-INF/05
6
C
ING-INF/06
6
C
MED/33 ,M-EDF/02
6
C
BIO/09 ,MED/01
6
C
MED/18 ,MED/37
6
C
ING-INF/05
6
C
ING-INF/05
6
C
ING-INF/05
English B1
3
E
-
Final exam
6
E
-
Modules Credits TAF SSD
Between the years: 2°- 3°
12
D
-
Between the years: 1°- 2°- 3°
Altre attività formative: lo studente può scegliere tra le 2 seguenti opzioni:
a) 2 CFU di seminari - di cui 1 CFU al 1 anno e 1 CFU al 2 anno - e 7 CFU di tirocinio al 3 anno; 
b) 9 CFU di tirocinio al 3 anno. Non sono previste ulteriori opzioni.
7
F
-
9
F
-

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 (2024/2025)

Teaching code

4S009890

Credits

6

Coordinator

Marco Cristani

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Teoria

Credits

4

Period

Semester 2

Academic staff

Marco Cristani

Lessons timetable
Laboratorio

Credits

2

Period

Semester 2

Academic staff

Dong Seon Cheng

Lessons timetable

Learning objectives

The aim of the course is to provide the student with an introduction to the basic concepts of artificial intelligence and its main research areas.
The student will acquire analytical skills and elementary design skills in specific areas such as research, logic computation, probabilistic and graphical models, machine learning.

Prerequisites and basic notions

Mathematical analysis I

Program

- Introduction: what is AI. Historical and disciplinary framework of AI. Generic structure of a decision agent. - Data representation. Discrete, continuous, sequential and structured attributes (Examples: representation of texts, images, industrial signals, financial series, acoustic signals, videos. - Recall of minimum statistical elements. - Recall of minimal elements of mathematics. - Foundations of Bayesian probabilistic theory. - Feature extraction and selection - Model selection - Learning of generative and discriminative models, Types of learning. - Unsupervised learning. Neural networks and deep learning: architecture, dynamics and learning. Various examples.

Didactic methods

Lectures and laboratory sessions in Python

Learning assessment procedures

Written test on all teaching topics, through open questions and exercises. 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, students must demonstrate that they: - have understood the concepts underlying the theory of Artificial Intelligence; - be able to present your arguments in a precise and organic way; - know how to apply the knowledge acquired to solve application problems presented in the form of questions and exercises.

Criteria for the composition of the final grade

The grade of the written text will be up to a maximum of 33 points, saturating at 30 cum laude.

Exam language

Italiano o Inglese nessuna preferenza