Affective computing deals systems that process or simulate human affect, including emotions, through the analysis of human behavior. Increasingly applications for the detection of affect and emotions are moving toward the use of deep learning and other black box models. Due to the strongly personal nature of behavior analysis and significant variations in emotional expression between individuals, models should be made transparent and explainable. In this seminar, we will learn about state-of-the-art approaches and associated challenges for explainable affective computing. Since affective computing typical deals with multimodal data, this seminar will look at explainability methods for visual, audio, and text data. Additionally, we will explore emerging research in explainability for multimodal systems. This seminar will include both theoretical and practical methods. Therefore, you will be expected to have a working knowledge of implementing and working with deep learning models in TensorFlow and Keras.
Frequency | Weekday | Time | Format / Place | Period |
---|
Module | Course | Requirements | |
---|---|---|---|
39-Inf-EGMI Ergänzungsmodul Informatik | vertiefendes Seminar 1 | Ungraded examination
|
Student information |
vertiefendes Seminar 2 | Ungraded examination
|
Student information | |
vertiefendes Seminar 3 | Ungraded examination
|
Student information | |
vertiefendes Seminar 4 | Ungraded examination
|
Student information | |
39-M-Inf-AI-adv Advanced Artificial Intelligence | Advanced Artificial Intelligence: Seminar 1 | Study requirement
|
Student information |
Advanced Artificial Intelligence: Seminar 2 | Graded examination
|
Student information | |
39-M-Inf-INT-adv Advanced Interaction Technology | Advanced Interaction Technology: Seminar 1 | Study requirement
|
Student information |
Advanced Interaction Technology: Seminar 2 | Graded examination
|
Student information | |
39-M-Inf-VKI Vertiefung Künstliche Intelligenz | Spezielle Themen der Künstlichen Intelligenz | Ungraded examination
Graded examination |
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there: