Learning Outcome
This course aims to teach the theories, methodologies and applications surrounding affective computing in an interdisciplinary perspective. After successfully completing the course, students understand affective interactions and its implications to human-computer interaction, learn to apply methods for collection, analysis and evaluations of affective behavior data. They demonstrate knowledge on computational analysis, synthesis and recognition of human affective behavior data, and designing emotionally sensitive interactive technologies such as interactions with virtual agents, robots and games. They gain hands-on experience with the frameworks for human affect and behavior understanding, and awareness of potential bias in data as well as possible dangers of dealing with sensitive personal data.
Course Content
- Introduction to affective computing with an overview of its application in entertainment, health and pedagogy
- Emotion theories: psychology, cognitive science and neuroscience
- Discussion on ways to make machines “have” emotions
- Experimental design, methodology, and analysis
- Emotion and the brain
- Bodily expression of emotions
- Synthesis of emotional behavior
- Emotion and social interaction
- Personality and cultures
- Emotion recognition in text, speech and face
- Hands-on programming experience for affective computing
- Bias and ethics of affective computing
Recommended Requirements
- Programming skills
- Statistical Machine Learning or Introduction to Artificial Intelligence
- Dozent*in: Cigdem Turan