Nell'ambito del Dottorato Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments si segnala il corso di dottorato “Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence” a partecipazione libera e gratuita anche per studenti di altri corsi di dottorato e tenuto in lingua inglese.
Il corso si terrà nei giorni 16 - 17 - 18 - 19 - 20 Marzo 2015 presso il DITEN Via dell’Opera Pia 11, 4° piano (edificio ex CNR) in Aula Mansarda. Orario 10-13, 15-17.
Si richiede l'iscrizione inviando una mail a Carlo.Regazzoni@unige.it e a segr_isip@ginevra.dibe.unige.it
Description of the Course:
The course aims at providing PhD Candidates knowledge on basic tools in data fusion domain together with more advanced theories for representing, modelling and automatically interpreting interactions occurring between users, between users and artificial systems etc. within a smart cognitive environment. A Bayesian approach tailored for cognitive dynamic systems paradigm is used as a main methodological track in the course. In particular this module aims at:
- providing a common Cognitive Data Fusion framework to identify and to describe methodologies and techniques for integrating multisensorial contextual data within a cognitive artificial embodied
- describing probabilistic representation techniques for modeling context based and adaptive interactions; discussing probabilistic centralized and distributes probabilistic signal processing techniques appropriate for dynamic object refinement, situation assesment and impact prediction:
- showing applications of above methodlogies and techniques to design Cognitive Dynamic Systems provided of inference and machine learning capabilities in smart environments.
- actively involving participants to develop their capabilities to relate acquired knowledge with their research objectives - case studies will be considered related to cognitive physical security environments, cognitive first person systems and cognitive radio in addition to proposed participating PhD students research problems