Biological and Artificial Intelligent Systems
A.Y. 2020/2021
Learning objectives
The course aims to provide students with the basics of Artificial Intelligence with an interdisciplinary approach to the scientific and philosophical research, supported by practical activities.
Expected learning outcomes
Knowledge and understanding
At the end of the course the student
- knows the fundamental elements of Artificial Intelligence and can understand the frontiers
- knows the fundamental methods and how use software platforms to develop some machine learning algorithms
- understands and can discuss about philosophical and technological issues
Ability to apply knowledge and understanding
- can apply the acquired knowledge to the development of fundamental algorithms
- can discuss about Artificial Intelligence and the related philosophical problems
- can interpret Artificial Intelligence methods by relating them to their processing contexts and their respective functions
- can apply the acquired knowledge to concepts of different interdisciplinary nature.
At the end of the course the student
- knows the fundamental elements of Artificial Intelligence and can understand the frontiers
- knows the fundamental methods and how use software platforms to develop some machine learning algorithms
- understands and can discuss about philosophical and technological issues
Ability to apply knowledge and understanding
- can apply the acquired knowledge to the development of fundamental algorithms
- can discuss about Artificial Intelligence and the related philosophical problems
- can interpret Artificial Intelligence methods by relating them to their processing contexts and their respective functions
- can apply the acquired knowledge to concepts of different interdisciplinary nature.
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
Lessons will be taught asynchronously online. Some online meetings will be held, including all the students or groups.
Course syllabus
Didactic Unit A
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.
Didactic Unit B
Program for attending and non-attending students
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.
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.
Didactic Unit B
Program for attending and non-attending students
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 statistics and logics. Use of basic information tools. Basic knowledge of algorithms and mathematics related to functions representation.
Teaching methods
Theoretical lessons and practical applications. Final project.
Teaching Resources
Didactic Unit A
· "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
Didactic Unit B
· Lecture notes by the teacher
· Other material communicated from time to time in class
· "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
Didactic Unit B
· Lecture notes by the teacher
· Other material communicated from time to time in class
Assessment methods and Criteria
Written + oral: The exam consists of a written test and an oral test, both of which are mandatory. The written exam involves the design and development of a software project relating to the topics covered during the course (development of an artificial intelligent system). The oral exam will consist of an interview on the topics in the program and the project presented, aimed at ascertaining that the student has completed the test independently and has acquired the required knowledge and skills
Modules or teaching units
Unita' didattica A
INF/01 - INFORMATICS - University credits: 3
Lessons: 20 hours
Unita' didattica B
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)