Artificial Intelligence
A.Y. 2021/2022
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.
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.
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.
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
During the ongoing covid emergency, the course syllabus will be maintained with the following changes made to enhance the effectiveness of the online version of the course, which was originally designed for face-to-face teaching.
The lessons will be held in dual mode. The face-to-face lessons will allow the participation of students connected with MSTeams as well as students in the classroom.
The recordings of the lessons will remain available for the entire academic year.
Online environments used:
Ariel (Artificial Intelligence, Prof. Folgieri): https://ariel.unimi.it/
Teams (keycode ofa9g3d)
Students wishing to participate in face-to-face lessons must refer to the following University provisions: https://www.unimi.it/en/study/bachelor-and-master-study/following-your-programme-study/teaching-activities-campus
Students wishing to participate in MSTeams lessons must refer to the following technical guides: https://www.unimi.it/en/study/student-services/technology-and-online-services/microsoft-office-365-education
To participate in the exam sessions, students must refer to the following provisions:
https://www.unimi.it/en/study/bachelor-and-master-study/following-your-programme-study/sitting-exams
The lessons will be held in dual mode. The face-to-face lessons will allow the participation of students connected with MSTeams as well as students in the classroom.
The recordings of the lessons will remain available for the entire academic year.
Online environments used:
Ariel (Artificial Intelligence, Prof. Folgieri): https://ariel.unimi.it/
Teams (keycode ofa9g3d)
Students wishing to participate in face-to-face lessons must refer to the following University provisions: https://www.unimi.it/en/study/bachelor-and-master-study/following-your-programme-study/teaching-activities-campus
Students wishing to participate in MSTeams lessons must refer to the following technical guides: https://www.unimi.it/en/study/student-services/technology-and-online-services/microsoft-office-365-education
To participate in the exam sessions, students must refer to the following provisions:
https://www.unimi.it/en/study/bachelor-and-master-study/following-your-programme-study/sitting-exams
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.
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.
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 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, 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.
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
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)