Data Science
Affective Computing
- Class 30
- Practice 30
- Independent work 120
Course title
Affective Computing
Lecture type
Obligatory
Course code
20-02-027
Semester
3
ECTS
6
Lecturers and associates
Course objectives
The main objective of the course is to familiarize students with the basics of affective computing and to enable them to independently apply methods of analysis, synthesis and recognition of affective states. Through this course, students will gain theoretical and practical knowledge of this interdisciplinary field, which includes knowledge of the characteristics of various modalities of affective expression: psychophysical, voice, verbal and facial. The emphasis of the subject will be on the practical application and development of models for automated recognition of affective states in industries.
Content
Introduction to Affective Computing. Affective conditions. Psychophysiologic expression. Voice and verbal expression. Facial Expression. Analysis of Affective Conditions. Synthesis of affective states. Human-computer interactions. Applications, Trends, Future, Ethics. Presentations of student projects.
Required reading
The materials used during the lectures and practical sessions (Powerpoint slides and Jupyter notebooks)
Additional reading
Calvo, R. A., D'Mello S. K., Gratch J., Kappas, A. The Oxford Handbook of Affective Computing. Oxford University Press, 2014. ISBN: 9780199942237.
Scherer, K. R., Bänziger, T., Roesch, E. A. Blueprint for Affective Computing. Oxford University Press, 2010. ISBN: 9780199566709.
Davidson, R. J., Scherer, K. R., Goldsmith, H. H. Handbook of Affective Sciences. Oxford University Press, 2009. ISBN: 9780195377002.
Picard, R. W. Affective Computing. MIT Press, 2000. ISBN: 9780262661157.
- Study program duration
- 4 semesters (2 years)
- Semester duration
- 15 weeks of active teaching + 5 examination weeks
- Total number of ECTS points
- 120
- Certifications obtained during studies
-
IT SMF – ITIL Foundation
- Title
- struč.spec.ing.comp. (Professional Master of Computer Engineering with sub-specialization in Data Science)
Minimal learning outcomes
- Explain the methodology and purpose of automated recognition, analysis and synthesis of affective states
- Differentiate the types of affective states
- Analyze the features of psycho-physiological expression
- Analyze voice and verbal expression features
- Analyze facial expression features
- Build the basis of a model/system for emotion analysis/synthesis which uses one of the affective computing modalities that we went through in the course.
Preferred learning outcomes
- Judge which method is best for a particular problem
- Choose the optimal representation of affective states for a particular problem
- Critically evaluate the features of psycho-physiological expression and find out for which type of problem it is the most appropriate feature
- Critically evaluate the features of voice and verbal expression and find out for which type of problem it is the most appropriate feature
- Critically evaluate the features of facial expression and find out for which type of problem it is the most appropriate feature
- Build a model/system for emotion analysis/synthesis which uses one of the affective computing modalities that we went through in the course.
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