Artificial Intelligence and Data Analysis
A.Y. 2021/2022
Learning objectives
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.
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.
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
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
More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation.
Course syllabus
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 and the business fields
o AI management, present and future challenge of AI implementation
o XAI (eXplainable Artificial Intelligence)
o Ethics and AI
o AI and cognitive biases
o AI and functional areas
o Human Centered 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 and the business fields
o AI management, present and future challenge of AI implementation
o XAI (eXplainable Artificial Intelligence)
o Ethics and AI
o AI and cognitive biases
o AI and functional areas
o Human Centered AI
Prerequisites for admission
No prior knowledge is required; basic informatics knowledge would be useful for the complete understanding of the main topics.
Teaching methods
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. Lessons will be streamed on Microsoft Teams and also made available for students on the course website. One time a month the lesson will be also in presence.
Teaching Resources
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)
https://www.edizioniedra.it/Il_medico_40.aspx
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)
https://www.edizioniedra.it/Il_medico_40.aspx
Assessment methods and Criteria
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)
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)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 9
Lessons: 60 hours
Professors:
Cuculo Vittorio, Moioli Fabio, Triberti Stefano