Artificial Intelligence
A.Y. 2026/2027
Learning objectives
Students will acquire in-depth knowledge and skills in the history of science, with particular focus on the evolution of Artificial Intelligence within Information Technology and its intersection with other sciences.
Expected learning outcomes
Students will acquire the ability to:
- evaluate critically the source of information and the reliability of data in the field of intelligent artificial systems' training and the responses provided by such systems;
- apply reasoning skills in diverse scientific contexts that characterise the developments of Artificial Intelligence and its applications across various sectors;
- use relational, communicative and organisational skills also in highly complex contexts and in the management of group work;
- transmit the skills obtained also in non-specialist contexts;
- reflect on their own skills and evaluations;
- independently investigate a philosophical position or theoretical thesis.
- evaluate critically the source of information and the reliability of data in the field of intelligent artificial systems' training and the responses provided by such systems;
- apply reasoning skills in diverse scientific contexts that characterise the developments of Artificial Intelligence and its applications across various sectors;
- use relational, communicative and organisational skills also in highly complex contexts and in the management of group work;
- transmit the skills obtained also in non-specialist contexts;
- reflect on their own skills and evaluations;
- independently investigate a philosophical position or theoretical thesis.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Program for attending and non-attending students
Intelligent systems: characteristics, differentiations between biological and artificial systems.
History of AI (Artificial Intelligence). Logics. Boolean Logic: application in AI (exercises). GOFAI, strong and weak AI: supporters, motivations, supporting tests. The Turing Test: principles and exercises. Criticism of the Turing Test: Searle and Others. AI philosophy: the main exponents.
Artificial intelligence and machine learning: machine learning. Mathematical models, algorithms, examples.
Artificial systems that are inspired by biological ones. Genetic algorithms, an overview. Artificial Neural Networks. Brain-Computer Interface. Theoretical models and practical examples.
Online environment available at: https://rfolgieriai.ariel.ctu.unimi.it/v5/Home/default.aspx
Students taking the 6 cfu syllabus must attend the first 40 hours of lectures, those taking the 9 cfu syllabus attend the full 60 hours of lectures.
Lecture recording: No
Teaching proposal for non-attending students: 2 lectures dedicated to non-attending students by videoconference (Teams).
These lectures will be remote, recorded and made available in the online environment. Full details in https://rfolgieriai.ariel.ctu.unimi.it/v5/Home/default.aspx
Validity of the programme: 1 academic year, as per teaching regulations of the degree course
Intelligent systems: characteristics, differentiations between biological and artificial systems.
History of AI (Artificial Intelligence). Logics. Boolean Logic: application in AI (exercises). GOFAI, strong and weak AI: supporters, motivations, supporting tests. The Turing Test: principles and exercises. Criticism of the Turing Test: Searle and Others. AI philosophy: the main exponents.
Artificial intelligence and machine learning: machine learning. Mathematical models, algorithms, examples.
Artificial systems that are inspired by biological ones. Genetic algorithms, an overview. Artificial Neural Networks. Brain-Computer Interface. Theoretical models and practical examples.
Online environment available at: https://rfolgieriai.ariel.ctu.unimi.it/v5/Home/default.aspx
Students taking the 6 cfu syllabus must attend the first 40 hours of lectures, those taking the 9 cfu syllabus attend the full 60 hours of lectures.
Lecture recording: No
Teaching proposal for non-attending students: 2 lectures dedicated to non-attending students by videoconference (Teams).
These lectures will be remote, recorded and made available in the online environment. Full details in https://rfolgieriai.ariel.ctu.unimi.it/v5/Home/default.aspx
Validity of the programme: 1 academic year, as per teaching regulations of the degree course
Prerequisites for admission
Basic knowledge of statistics and logic. Familiarity with computer systems. Basic knowledge of simple mathematical expressions (representation of functions).
Teaching methods
Theoretical and practical lessons, class discussions, analysis of research works.
Teaching Resources
· "ARTIFICIAL INTELLIGENCE - Vol. 2, a modern approach", 2nd edition, by Peter Norvig and Stuart Russel, published by Pearson
· Lecture notes by the teacher
· Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements
· Other material communicated from time to time in class
· Lecture notes by the teacher
· Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements
· Other material communicated from time to time in class
Assessment methods and Criteria
Midterm tests and oral discussion aiming at verifying the acquired knowledge. Students are expected to show a deep understanding of problems related to AI and the ability to discuss and compare different perspectives related to the discipline, also in the light of philosophy approaches.
Students are also expected to be able to communicate properly the acquired knowledge.
Students are also expected to be able to communicate properly the acquired knowledge.
Modules or teaching units
Parte A e B
INFO-01/A - Informatics - University credits: 6
Lessons: 40 hours
Parte C
INFO-01/A - Informatics - University credits: 3
Lessons: 20 hours
Professor(s)
Reception:
Wednesday 11 am, also via Skype or Teams (contact me via e-mail)
Via Teams, Skype or in Via Festa del Perdono 7, Dipartimento di Filosofia, cortile Ghiacciaia, secondo piano (please, take an appointment via e-mail)