First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
COMPUTER SCIENCE
Course unit
ARTIFICIAL INTELLIGENCE
SCP6076337, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
COMPUTER SCIENCE
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2018/19
N0
bring this page
with you
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination ARTIFICIAL INTELLIGENCE
Website of the academic structure http://informatica.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
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

Lecturers
Teacher in charge ALESSANDRO SPERDUTI INF/01
Other lecturers LAMBERTO BALLAN 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
Hours of
Individual study
Shifts
Laboratory 1.0 8 17.0 No turn
Lecture 5.0 40 85.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
2 a.a. 2017/2018 01/10/2017 28/02/2019 PINI MARIA SILVIA (Presidente)
AIOLLI FABIO (Membro Effettivo)
CONTI MAURO (Membro Effettivo)
MARCHIORI MASSIMO (Membro Effettivo)
SPERDUTI ALESSANDRO (Membro Effettivo)

Syllabus
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