Human-centred Machine Perception

The ability to automatically perceive and interpret human behavior is crucial for adaptive intelligent technology (e.g., social robots or tutoring systems). Not only can it improve user understanding in applications, but it also holds the potential for novel scientific insights about human behavior in the real-world. This course covers relevant theories and principles for developing Human-centred Machine Perception systems that leverage sensor data (e.g., from cameras, microphones, or wearables) in individual, dyadic, and group settings. Topics covered include: data collection, annotation, implementation, and evaluation of such systems, as well as relevant theories about human behavior and psychology (e.g., vocal expression, group dynamics, and human emotion).

The final grade of the course consists of the following components:

• Digital Exam (weighting 60%)

• Individual Assignments: assignments applying advanced machine learning techniques in clearly scoped Human-centred Machine Perception tasks (weighting 40%)

Final grade calculation = 0.6 * Digital Exam + 0.4 * Individual Assignments

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Digital Exam: Resit opportunity

• Individual Assignments: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).