First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SC01103965, A.A. 2015/16

Information concerning the students who enrolled in A.Y. 2015/16

Information on the course unit
Degree course Second cycle degree in
SC1176, Degree course structure A.Y. 2014/15, A.Y. 2015/16
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Number of ECTS credits allocated 8.0
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 8.0

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

Type of hours Credits Teaching
Hours of
Individual study
Lecture 8.0 60 140.0 No turn

Start of activities 01/10/2015
End of activities 28/01/2016
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Examination board not defined

Prerequisites: The student should be familiar with basic concepts in Probability and Logic. It is also advisable to have basic knowledge of Programming and Algorithms.
Target skills and knowledge: The course covers fundamental concepts and techniques of some of the main approaches, belonging to the Artificial Intelligence area, to solve difficult computational problems. Specifically, the following topics will be covered: Problem Solving, Adversary Search, Knowledge and Reasoning (with and without Uncertainty), Planning, elements of Constraint Satisfaction Problems and Machine Learning.
In order to allow the student to understand the difficulties typically encountered in developing an Artificial Intelligence application, the student (or a team of students) is asked to develop a small application project.
Examination methods: The student must pass a written examination and, if deemed necessary by the teacher, an oral examination. In addition, the student is expected to develop, in agreement with the teacher, a small applicative project.
Assessment criteria: The text of the written exam includes some questions that aim to assess the level of learning reached by the student concerning the concepts taught in the course and the student's ability to perform critical analysis on them. In the event that the assessment of the written exam is not satisfactory for the student, the teacher may supplement the written examination with an oral examination to better assess the level of learning of the student.
The project evaluation considers the ability of the student to identify a case study, and to carry out, in autonomy, a design and a realization of appropriate quality.
Course unit contents: The course will cover the topics listed below:
- Introduction, Motivations, and Intelligent Agents Architectures;
- Problem Solving and Elements of Constraint Satisfaction Problems;
- Adversary Search;
- Knowledge Representation by Propositional and First Order Logic, Inference in Logic;
- Planning;
- Uncertainty, Probabilistic Reasoning;
- Elementrs of Machine Learning.
Planned learning activities and teaching methods: The course consists of lectures.
Additional notes about suggested reading: Slides presented during the lectures are made ‚Äč‚Äčavailable to students as reference material.
Textbooks (and optional supplementary readings)
  • Russell, Stuart J.; Norvig, Peter, Artificial intelligence: a modern approach. Englewood Cliffs: NJ, Prentice-Hall, 2010. Cerca nel catalogo