MULTIMEDIA SIGNAL PROCESSING FOR AUTONOMOUS SYSTEMS

iten
Codice
104780
ANNO ACCADEMICO
2021/2022
CFU
2.5 cfu al 2° anno di 10378 INTERNET AND MULTIMEDIA ENGINEERING (LM-27) GENOVA
SETTORE SCIENTIFICO DISCIPLINARE
ING-INF/03
LINGUA
Inglese
SEDE
GENOVA (INTERNET AND MULTIMEDIA ENGINEERING)
periodo
2° Semestre
moduli
Questo insegnamento è un modulo di:
materiale didattico

PRESENTAZIONE

OBIETTIVI E CONTENUTI

OBIETTIVI FORMATIVI

The course is aimed at providing machine learning basic and advanced techniques for data driven signal processing models to be used within autonomous systems design. In particular, perception and control modules in autonomous systems rely more and more on signal processing approaches whose parametrization can be learned from observing multimedia heterogeneous signals produced by the artificial system while performing specific tasks. The course analyses data acquisition and processing tradeoffs between edge and cloud resources on the basis of real-time, computational and energy consumption requirements. Specific attention will be devoted to high dimensional data processing on the edge (with real practical examples in Python), showing how deep learning approaches can be adapted and optimized for working with limited computational capabilities.

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

  • Learning of representations from heterogeneous raw data
  • Principles of supervised learning
  • Elements for different methods for deep learning: convolutional networks and recurrent networks
  • Edge computing principles and limitations – computational aspects
  • Theoretical knowledge of and practical experience of training networks for deep learning including optimization using stochastic gradient descent
  • New progress in methods for deep learning: Generative Adversarial Networks, Variational Autoencoders, Flow-based models, Long short-term memory networks
  • Analysis of models and representations for automatic decision making for autonomous systems (deep reinforcement learning)
  • Learning of collaborative models for multiple autonomous systems
  • Transfer learning with representations for deep learning
  • Application examples of edge deep learning for real autonomous systems

MODALITA' DIDATTICHE

The lessons alternate theoretical explanations with practical exercises. Theoretical explanations are frequently exemplified with the analysis, execution, and debugging of code fragments directly on the teacher's PC. All the material seen in class (slides and practical examples) is shared through the AulaWeb and Teams platforms. Students can interact directly with the teacher during lessons or through the Teams platform.

DOCENTI E COMMISSIONI

Ricevimento: on request

LEZIONI

MODALITA' DIDATTICHE

The lessons alternate theoretical explanations with practical exercises. Theoretical explanations are frequently exemplified with the analysis, execution, and debugging of code fragments directly on the teacher's PC. All the material seen in class (slides and practical examples) is shared through the AulaWeb and Teams platforms. Students can interact directly with the teacher during lessons or through the Teams platform.

Orari delle lezioni

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

ESAMI

MODALITA' D'ESAME

Development and presentation of practical project work.

Calendario appelli

Data Ora Luogo Tipologia Note
18/02/2022 09:00 GENOVA Esame su appuntamento
16/09/2022 09:00 GENOVA Esame su appuntamento