The teaching aims to introduce the fundamental concepts concerning Artificial Intelligence, illustrating the main methods and applications. It also intends to provide the student with the philosophical and sociotechnical bases of the concept of Artificial Intelligence, with particular attention to the role played by the same in the development of Cognitive Sciences. Secondly, the course intends to develop critical knowledge on the psychological and social factors related to the implementation of Artificial Intelligence in the real world. The knowledge of the methods used by Artificial Intelligence in the various fields of application is considered of utmost importance, deepening the algorithmic aspects of each topic. In the presentation of the course contents, particular attention will be paid to methods and applications relevant to data analysis, in order to provide the student with an understanding of the use of Artificial Intelligence as a powerful knowledge discovery tool.
Expected learning outcomes
The student will have to fully learn the methods underlying the different types of AI applications, knowing how to critically compare the different algorithmic solutions of the same problem. Furthermore, the student must be able to deal with real AI management cases in an application context. The communication of the knowledge learned must take place through a formally correct lexicon.
Lesson period: Second semester
(In case of multiple editions, please check the period, as it may vary)
The course presents an overview of the main topics of Artificial Intelligence: o what is Artificial Intelligence (AI) o history of AI o philosophy of AI o From Human Computer Interaction to Human Computer Confluence o Embodied Cognition and Strong/Weak AI o Singularity and Transhumanism o the concept of Intelligence from psychology to AI o applied AI, with a focus on the medical/healthcare field o AI management, present and future challenge of AI implementation o XAI (eXplainable Artificial Intelligence) o Intelligent agents o search and problem solving strategies o knowledge representation o logic: propositional, first order, fuzzy o uncertainty management o learning o natural language processing o perception o robotics Also, the course will place particular attention on data analysis applications.
Prerequisites for admission
No prior knowledge is required; basic informatics knowledge would be useful for the complete understanding of the main topics.
The course consists in lectures supported by slides. Also other online resources will be accessed, and students will be presented with tools for computational analysis.
Il materiale didattico consiste nelle slides che verranno depositate su ARIEL e nei volumi:
Stuart J. Russell, Peter Norvig, Intelligenza artificiale. Un approccio moderno, 1998, ed. L. Carlucci Aiello, UTET Università, ISBN 9788877504067
The teaching material consists of the slides uploaded on ARIEL and in the textbooks:
Stuart J. Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 2009, Pearson Education (US), EAN: 9780136042594
Gabriella Pravettoni, Stefano Triberti IL MEDICO 4.0 - Come cambia la relazione medico-paziente nell'era delle nuove tecnologie, EDRA (2019)
Verification of the student's knowledge will be assessed through an oral test; The candidate will be presented with questions on the entire course contents. The candidate will be evaluated on his/her ability to respond clearly, critically and rigorously to the questions, possibly with the aid of formulas or graphs. There will be no intermediate exams. No additional materials will be needed to sustain the exam, paper and pen will be provided by the professors if needed. The exam score is expressed in thirtieths (the exam is passed with a minimum score of 18)