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

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.

1° Year

ModulesCreditsTAFSSD
6
B
INFO-01/A
12
B
IINF-05/A
12
B
INFO-01/A

2° Year  It will be activated in the A.Y. 2027/2028

ModulesCreditsTAFSSD
6
B
INFO-01/A
Final exam
24
E
-
It will be activated in the A.Y. 2027/2028
ModulesCreditsTAFSSD
6
B
INFO-01/A
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2026/2027
- 2nd year - AI & cloud, Advanced programming for AI - delivered in 2027/2028
- 1st and 2nd year - Computer vision & deep learning - delivered in 2026/2027 and in 2027/2028
6
B
IINF-05/A
6
B
INFO-01/A
6
B
INFO-01/A
6
B
IINF-05/A
6
B
INFO-01/A
6
B
IINF-05/A
Between the years: 1°- 2°
2 modules among the following (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2026/2027
- 2nd year - AI & cloud, Advanced programming for AI, Visual intelligence - delivered in 2027/2028
- 1st and 2nd year - Computer Vision & deep learning, Statistical learning - delivered in 2026/2027 and in 2027/2028
6
C
IINF-05/A
6
C
INFO-01/A
6
C
INFO-01/A
6
C
IINF-05/A
6
C
INFO-01/A
6
C
IINF-05/A
6
C
INFO-01/A
6
C
IBIO-01/A
Between the years: 1°- 2°
Two modules among the following
A.A. 2026/2027: Complex Systems not activated
6
C
IINF-05/A
6
C
INFO-01/A
6
C
PHYS-02/A
6
C
INFO-01/A
6
C
MATH-05/A
6
C
MATH-06/A
6
C
IINF-05/A ,INFO-01/A
6
C
INFO-01/A
6
C
PHYS-02/A
6
C
INFO-01/A
6
C
MATH-03/B
6
C
INFO-01/A
Between the years: 1°- 2°
One module among the following
6
C
STAT-04/A
6
C
GIUR-04/A
6
C
GIUR-11/A
6
C
PHIL-02/A
6
C
GIUR-05/A
6
C
GIUR-14/A
6
C
GIUR-02/A
6
C
GIUR-01/A
Between the years: 1°- 2°
Further activities: 3 CFU training and 3 CFU further language skill or 6 CFU training. International students (i.e. students who do not have an Italian bachelor’s degree) must compulsorily gain 3 CFU of Italian language skills (at least A2 level) and 3 CFU training
6
F
-
Between the years: 1°- 2°
12
D
-

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

Computational Game Theory  (2026/2027)

Teaching code

4S010687

Teacher

Ferdinando Cicalese

Coordinator

Ferdinando Cicalese

Credits

6

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

INFO-01/A - Informatics

Period

I semestre dal Oct 1, 2026 al Jan 29, 2027.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

Many problems in computer science involve settings where multiple self-interested parties interact, e.g., resource allocation in large networks, online advertising, managing electronic marketplaces and networked computer systems. Computational (algorithmic) game theory complements economic models and solution concepts, to reason about how agents should act when the actions of other agents affect their utilities, with a focus to discuss computational complexity issues, and the use of approximation bounds for models where exact solutions are unrealistic. The course aims to give students an introduction to the main concepts in the field of computational game theory with representative models and (algorithmic) solution chosen to illustrate broader themes. Students will acquire the basic skills to design models and computer systems that performs optimally/well in some paradigmatic multiagent settings; and to reason about the design of mechanisms to incentivate self-interested users to behave in a desirable way.

Prerequisites and basic notions

Basic knowledge of discrete maths and calculus
Basic probability theory

Program

1. Introduction to strategic games, costs, payoffs; basic solution concepts; equilibria and learning in games; Nash equilibrium; repeated games; cooperative games. 2. Basic computational issues of finding equilibrium. 3. Repeatedly making decisions with uncertainty; learning, regret minimization and equilibrium. 3. Graphical games and connections to probabilistic inference in machine learning. 4. Elements of Mechanism Design; Auctions; distributed mechanism design.

Bibliography

Vai alla bibliografia
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 course is ogranized around 2 weekly lectures, homeworks assigned to clarify and deepen on the theory topics and some exercise sessions, where the solution to these exercises will be discussed.

Learning assessment procedures

The exam consists of a written test with open questions and multiple choice questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to verify a straightforward application of the elements studied in class. The "free-choice" exercises test the ability of students to re-elaborate these notions in "new" scenarios.
Depending on the number of students attending, the exam can be partially based on the discussion of a scientific article on the application of computational game theory.

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

The exam verifies that the students have acquired sufficient confidence and skill in the application of the basic game thoretic models and their solutions, and are able to contextualize them in novel multiagent scenarios.

Criteria for the composition of the final grade

In the case the exam includes two parts (e.g., written test and oral discussion of an article) the final grade will be computed by averaging the grades awarded to the two parts of the exam.

Exam language

Inglese / Italiano

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