Entry requirements

120 credits including 15 credits in mathematics and 60 credits in computer science/information systems, including 20 credits in programming/algorithms/data structures.

Learning outcomes

After successful completion of the course, a student should be able to

• implement computational techniques for detection and tracking of features describing human behaviour in different modalities of perception
• use machine learning methods for automatic detection of human behaviours and states
• determine appropriate design approaches to build social perception abilities such as recognising humans, behaviours and higher level social states and variables (e.g., emotions) based on low-level features from different modalities of perception
• describe basic principles of socially adaptive behaviour in robots and embodied interactive systems
• apply basic principles of design and evaluation of human-machine interaction
• evaluate the impact that affect recognition, behaviour detection, and similar technologies may have on ethical values like privacy and autonomy, and to suggest strategies for fulfiling values that are important for users and society at large, including minimisation of negative consequences

Content

Topics include face detection and tracking, facial feature detection and tracking, facial expression and gesture recognition, automatic analysis of multimodal behaviour, automatic inference of affect and social signals, reinforcement learning for adaptive machines, machine embodiment and behaviour generation, control and planning, human-agent and human-robot interaction.

Instruction

Lectures and assignments.

Assessment

Written assignments (2 credits), oral and written presentation of a project (3 credits).