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.

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 Ingegneria e scienze informatiche - 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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
12
B
ING-INF/05
12
B
ING-INF/05
6
B
ING-INF/05

2° Year   activated in the A.Y. 2020/2021

ModulesCreditsTAFSSD
6
B
ING-INF/05
6
B
INF/01
Other activities
4
F
-
Final exam
24
E
-
ModulesCreditsTAFSSD
12
B
ING-INF/05
12
B
ING-INF/05
6
B
ING-INF/05
activated in the A.Y. 2020/2021
ModulesCreditsTAFSSD
6
B
ING-INF/05
6
B
INF/01
Other activities
4
F
-
Final exam
24
E
-
Modules Credits TAF SSD
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

4S02789

Credits

6

Coordinator

Not yet assigned

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

To show the organization of the course that includes this module, follow this link:  Course organization

The teaching is organized as follows:

Teoria

Credits

4

Period

II semestre

Academic staff

Alessandro Farinelli

Laboratorio

Credits

2

Period

II semestre

Academic staff

Alessandro Farinelli

Learning outcomes

The class presents the main techniques for problem solving, based on the central paradigm of symbolic and probabilistic representations. The objective is to provide the students with the ability to design, apply and evaluate algorithms for difficult problems, meaning that their mechanical solution captures aspects of artificial intelligence or computational rationality.

At the end of the course the student must demonstrate to know and understand the main techniques for state space search, to understand the fundamental concepts related to constrained networks and to know the basic concepts related to probabilistic reasoning and reinforcement learning.

This knowledge will allow the student to: i) apply the state space search techniques to problems of different nature; ii) apply the main solution algorithms for constrained networks both in the context of satisfiability and optimization; iii) use the main solution techniques related to probabilistic reasoning, with particular emphasis on Bayesian networks, Markov decision processes and reinforcement learning.

At the end of the course the student will be able to: i) choose the most appropriate solution technique for different problems; ii) continue independently the studies in Artificial Intelligence, deepening the topics covered in class, both on other texts and on scientific publications.

Program

Problem solving as search in a state space; un-informed search procedures; heuristic search procedures; adversarial search.
Problem solving based on constraint processing (satisfaction and optimization); Solution techniques based on search (Backtracking, Branch and Bound) and inference (Join Tree Clustering, Bucket Elimination);
Intelligent agents: multi-agent systems, coordination.
Probabilistic reasoning: i) Bayesian networks (definitions, main concepts and inference methods); ii) Markov decision processes (definitions and main solution techniques); iii) reinforcement learning (basic concepts and solution
techniques, e.g. Q-Learning).
Implementing (through assisted software development) the main solution techniques presented during the course related to state space search and probabilistic reasoning.

Students can find teaching material and further information on this course at this link: http://profs.sci.univr.it/~farinelli/courses/ia/ia.html

Bibliography

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Teoria Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (Edizione 2) Prentice Hall 2003 0137903952
Teoria Rina Dechter Constraint Processing (Edizione 1) Morgan Kaufmann 2003 ISBN 978-1-55860-890-0
Teoria Richard S. Satto and Andrew G. Barto Reinforcement Learning: an introduction MIT press 1998 ISBN 0-262-19398-1

Examination Methods

The final grade for the IA module can be achieved with a single test or with partial tests.
The single written test will be done at the exam date.
The partial tests includes two written tests (one during the course and one at the end of the course) or a written test (done during the course) and a project (usually with a consistent programming part). The partial tests modalities includes a test in the programming laboratory (optional). This test aims at evaluating the software produced by the students during 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