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Scheda Riassuntiva
Anno Accademico 2021/2022
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 056890 - UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
Docente Bonarini Andrea
Cfu 5.00 Tipo insegnamento Monodisciplinare
Didattica innovativa L'insegnamento prevede  1.5  CFU erogati con Didattica Innovativa come segue:
  • Blended Learning & Flipped Classroom

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing - Civ (Mag.)(ord. 270) - MI (495) GEOINFORMATICS ENGINEERING - INGEGNERIA GEOINFORMATICA*AZZZZ056890 - UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - CR (263) MUSIC AND ACOUSTIC ENGINEERING*AZZZZ056890 - UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AZZZZ056890 - UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ056890 - UNCERTAINTY IN ARTIFICIAL INTELLIGENCE

Obiettivi dell'insegnamento

Reality is different from its model, and from this difference uncertainty arises. When facing problems in the real world, dealing with uncertainty is needed. This course presents powerful modeling tools for engineers and in general people needing to model complex phenomena with Artificial Intelligence techniques. Among the application areas, we mention: (big) data analysis, classification, automatic control, robotics, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), decision support, forecasting. This course will introduce rigorously the theoretical fundamentals of different modeling approaches, will put in evidence their application possibilities, by comparing different models, examples, and application cases, and will introduce design techniques for systems based on these technologies. This course takes the place of the Soft Computing course, offered for may years, keeping the original motivation, dealing with uncertainty, while some of the specific techniques once presented in Soft Computing take now space appropriate for the development they had in the last years in other courses and have been migrated in Machine Learning, Neural Networks and Deep Learning, and Data Mining. In this Uncertainty for Artificial Intelligence course are treated more in details issues, techniques, and modeling approaches more strictly related to modeling the intrinsic uncertanty that is present in the

Goals for this course are listed in the following.

- Presentation of the main issues in modeling the real world, including the possible sources of uncertainty and how it is possible to reduce their effect or cope with them.

- Presentation  of basic knowledge about some technologies used to model uncertainty namely: Uncertainty measures, Logic-based approaches, Fuzzy Systems, Possibility systems, and Probabilistic Graphical Models (e.g., Bayesian Networks and Hidden Markov Models).

- Presentation of tools to implement the mentioned technologies.

- Analysis of paradigmatic case studies to understand the applicability issues of the mentioned technologies.

- Development of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures, and processes including the mentioned technologies

- Development of the ability to learn autonomously both declarative and procedural knowledge (thanks to innovative teaching methods, such as flipped class, and blended learning).

 


Risultati di apprendimento attesi

Acquisition of basic knowledge about some technologies to treat uncertainty in AI systems, namely: Uncertainty measures, Logic-based approaches, Fuzzy Systems, Possibility systems, and Probabilistic Graphical Models (e.g., Bayesian Networks and Hidden Markov Models). (DD1)

Acquisition of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures and processes for the mentioned technologies. (DD2, DD3)

Acquisition of basic operational abilities to implement the mentioned technologies. (DD2)

Acquisition of the ability to present both the knowledge, the process, and the proposed solutions, as well as to analyze results and data (DD4)

Acquisition of the ability to learn autonomously both declarative and procedural knowledge.


Argomenti trattati
  • Uncertainty sources that acffect models: typology, issues, and modeling approaches.
  • Measure-based uncertainty modeling
  • Logic-based uncertainty modeling
  • Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling, tools for fuzzy systems, design of fuzzy systems, applications.
  • Bayesian networks: basics, design, learning, evaluation, applications
  • Hidden Markov Models: basics, design, learning, evaluation, applications
  • Applications: motivations, choices, models, case studies.

Prerequisiti

 No specific background is required.


Modalità di valutazione

The evaluation consists of both a written exam where both theoretical competence and modeling skills will be tested, and small team exercise(s), developed and evaluated during the course, intended to develop competence while following the course, so that the final written exam may be faced properly, with less concentrated effort. Attendance to lessons is important mainly to develop the modeling skills,  which cannot be acquired from books, but only from experience, and to participate to the development of team exercises in a way better than remote interaction, which is, however, still possible.


Bibliografia
Risorsa bibliografica obbligatoriaSlides, links to free material, book suggestions are provided through the course web page on BEEP http://beep.polimi.it
Note:

Students are strongly advised to consider ALL the resources, included books and not only the slides.


Software utilizzato
Nessun software richiesto

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
10:00
30:00
Esercitazione
10:00
25:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
20:00
30:00
Totale 40:00 85:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.7.2 / 1.7.2
Area Servizi ICT
07/07/2022