First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP6076337, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination ARTIFICIAL INTELLIGENCE
Website of the academic structure
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge ALESSANDRO SPERDUTI INF/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses INF/01 Computer Science 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 1.0 8 17.0 No turn
Lecture 5.0 40 85.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
4 a.a. 2019/2020 01/10/2019 30/09/2021 SPERDUTI ALESSANDRO (Presidente)
BALLAN LAMBERTO (Membro Effettivo)
AIOLLI FABIO (Supplente)
CONTI MAURO (Supplente)
3 a.a. 2018/2019 01/10/2018 30/09/2020 SPERDUTI ALESSANDRO (Presidente)
BALLAN LAMBERTO (Membro Effettivo)
AIOLLI FABIO (Supplente)
CONTI MAURO (Supplente)

Prerequisites: It is opportune to know basic notions of Probability Theory, Programming, and Algorithms.
Target skills and knowledge: The course will present the main techniques of some of the most important approaches in the Artificial Intelligence field for solving difficult problems. In particular, it will present techniques for solving problems by searching, constraint-based systems, knowledge representation and manipulation with and without uncertainty, planning, decision making, and some hints of machine learning.
It is required the development of a project for a single student or a group of students.
Examination methods: The student must overcome a written exam. Moreover, the student must develop a project.
Assessment criteria: The evaluation of the student verifies the knowledge of the student of the basic notions introduced in the course and his/her analysis capabilities.
The evaluation of the project considers the capability of the student of finding a specific case study, and his/her ability to develop autonomously all the project activities to tackle with the case study.
Course unit contents: The structure and the topics of the course will be described in the following:
- Introduction, Motivation, Intelligent Agents Architectures;
- Problem Resolution and Constraint-based Systems;
- Planning;
- Dealing with Uncertainty and Probabilistic Reasoning;
- Decision Making
- Hints of Machine Learning.
Planned learning activities and teaching methods: The course will have frontal lessons.
Additional notes about suggested reading: Additional material will be available on the course website.
Textbooks (and optional supplementary readings)
  • Stuart Russell, Peter Norvig, Artificial Intelligence: A modern approach. --: Prentice Hall, 2010. Cerca nel catalogo