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Dati Insegnamento
Stampe
Manifesto
Dati Insegnamento
Contesto
Anno Accademico 2024/2025
Corso di Studi Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology
Anno di Corso 1

Scheda Insegnamento
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 Descrizione SSD CFU
ING-INF/04 AUTOMATICA 5.0

Dettaglio
Scaglione Nome Programma dettagliato
Da (compreso) A (escluso)
A ZZZZ Ruiz Palacios Fredy Orlando, Fagiano Lorenzo Mario, Novara Carlo
manifestidott v. 1.10.0 / 1.10.0
Area Servizi ICT
09/02/2025