Affective computing

The course describes and analyses theory and techniques for the design of affective artificial agents that are able to:
- perceive the user's affective signals and extract their most significant cues
- infer, form such cues in a given context, user's affective state
- provide the appropriate feedback to the user
To such end, the course concerns: a rigorous introduction to the neurobiological and psychological models of emotions; stochastic processes and statistical machine learning and inference for modelling the dynamics of affect.

Upon completion of the course students will be able to:
1. Define the methodology and the most appropriate techniques for modelling affective agents dealing with uncertainty
2. Measure and analyse affective signals, either behavioural or physiological and extract affective cues
3. Design and implement simple affective agents to be exploited in different applications such as video surveillance, autonomous driving, robotics, entertainment.
These objectives are measured via a combination of three components: the project realisation, the project technical report and the oral discussion. The final grade is formed by assessing the software developed, the project report, and then using the oral discussion for fine tuning.