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Manifesto

Dettaglio Insegnamento

Contesto
Anno Accademico 2021/2022
Corso di Studi Dott. - MI (1387) Data Analytics and Decision Sciences
Anno di Corso 1

Scheda Insegnamento
Codice Identificativo 057332
Denominazione Insegnamento TIME-SERIES EXPLORATION WITH MACHINE AND DEEP LEARNING: FROM THEORY TO PRACTICE
Tipo Insegnamento MONODISCIPLINARE
Crediti Formativi Universitari (CFU) 5.0
Programma sintetico The course presents, in an integrated and comprehensive way, theoretical and practical aspects for the analysis and prediction of time-structured data. The course aspires at providing students an end-to-end pipeline for time-series analysis/prediction from data wrangling to model evaluation by also introducing novel "as-a-service" approaches in the field of time-series forecasting. Theoretical lectures will be complemented with practical lectures describing Python libraries, frameworks, and toolboxes for time-series analysis and prediction. Finally, a specific focus is given on hands- on application cases by considering three real-world application scenarios where theory and practice will be applied. In more detail, the course is organized into the following theoretical (T) and practical (P) lectures (2h): 1) Introduction to time-structured data (T) 2) Data wrangling for time-series (T) 3) Exploratory data analysis for time-series (T) 4) Tools and mechanisms for time-series wrangling and exploration (P) 5) Statistical models for time series prediction (T) 6) Hands-on statistical models for time series (P) 7) Machine learning for time series prediction (T) 8) Hands-on machine learning for time series (P) 9) Deep learning for time series prediction (T) 10) Hands-on deep learning for time series (P) 11) Measuring and evaluating the error (T) 12) Forecasting-as-a-service (T) 13) Hands-on applications in healthcare, finance and environmental monitoring (P)
Settori Scientifico Disciplinari (SSD) --

Dettaglio
Scaglione Docente Programma dettagliato
Da (compreso) A (escluso)
A ZZZZ Roveri Manuel
manifestidott v. 1.7.0 / 1.7.0
Area Servizi ICT
12/08/2022