Artificial Intelligence

A.Y. 2020/2021
Overall hours
Learning objectives
The courses included in the sector INF / 01 - Informatics aim to introduce the learners to the application of philosophical theories to technological IT fields according to the modern experimental approach.
In this context, the objective of the Artificial Intelligence course is the application of philosophical tools to empirical research in this IT field, through the illustration of concrete problems that can be tackled thanks to the acquired philosophical information and IT methodologies.
Expected learning outcomes
a. Knowledge and understanding
At the end of the Artificial Intelligence course, students will have acquired a high understanding of problems, and of discussion and comparison between the different theoretical perspectives in the disciplinary field, in light of the philosophical theories acquired in their studies.
b. Ability to apply knowledge and understanding
At the end of the course, students will be able to design IT solutions related to theoretical and practical problems, combining Artificial Intelligence methodologies and philosophical theories, having also gained communication skills of what has been learned.
Course syllabus and organization

Single session

Lesson period
First semester
Synchronous lessons will be taught on Teams (class code: l1qrag6) and for asynchronous lesson all the material will be provided by the platform Ariel.
Course syllabus
Program for attending and non-attending students
Intelligent systems: characteristics, differentiations between biological and artificial systems.
History of AI (Artificial Intelligence). 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, exercises, implementation of algorithms.
Artificial systems that are inspired by biological ones. Genetic algorithms, Artificial Neural Networks, Brain Computer Interface. Theoretical models and practical exercises.
Prerequisites for admission
Basic knowledge of statistics and logic. Familiarity with computer systems. Basic knowledge of algorithms and mathematical expressions related to the representation of functions.
Teaching methods
Theoretical and practical lessons taught in synchronous modality by Teams and asynchronous material on Ariel.
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
Assessment methods and Criteria
Midterm tests and final test with an 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 in the light of philosophy approaches.
Students are also expected to be able to design ICT solutions to theoretical and practical problems and to be able to communicate properly the acquired knowledge.
Unita' didattica A
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Unita' didattica B
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Unita' didattica C
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Wednesday 11 am. Considering the circumstances, via skype (nickname MTCUBE)
Currently via Skype. Under normal circumstances in Via Festa del Perdono 7, Dipartimento di Filosofia, cortile Ghiacciaia, secondo piano