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Anno Accademico |
2024/2025 |
Corso di Studi |
Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology |
Anno di Corso |
1 |
Codice Identificativo |
062776 |
Denominazione Insegnamento |
DEALING WITH UNCERTAINTY IN DATA-BASED LEARNING |
Tipo Insegnamento |
MONODISCIPLINARE |
Crediti Formativi Universitari (CFU) |
5.0 |
Programma sintetico |
Many science and engineering problems entail the derivation of mathematical models (learning) from prior information and data. A crucial aspect when learning models from data is handling the uncertainty caused by noisy and incomplete information, and consequently the computation of models with minimal uncertainty. Set Membership (SM) approaches provide a theoretical framework and practical tools to deal with these aspects. This course aims to introduce the general Set Membership estimation theory, and to describe solutions to machine learning problems, in settings like the estimation of models for dynamical systems, data-driven filters and controllers? design, and global black-box optimization, all supported by hands-on sessions. |
Settori Scientifico Disciplinari (SSD) |
Codice SSD
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Descrizione SSD
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CFU
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ING-INF/04
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AUTOMATICA
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5.0
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Scaglione
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Nome
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Programma dettagliato
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Da (compreso)
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A (escluso)
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A
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ZZZZ
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Ruiz Palacios Fredy Orlando, Fagiano Lorenzo Mario, Novara Carlo
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