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 is used as a main methodological track in the course. In particular this module aims at :- providing a common framework to identify and to describe methodologies and techniques for integrating multisensorial contextual data by using Data Fusion paradigms and techniques - providing a common framework for defining behavioural artificial models for context based, adaptive and personalized decision steps used by cognitive system to address and react with respect to different contextual working situations. - Showing examples and applications of specific techniques within cognitive telecommunication systems by means of description of two main case studies: cognitive radio and multisensor/multimodal cognitive human-machine interfaces in smart spaces.