Deep Learning for Understanding the Human
We will explore how deep learning approaches can be used for perceiving and interpreting the state and behavior of human beings in images, video, audio, and text data. The course will cover how convolutional, recurrent and generative neural networks can be used for applications of face recognition, eye tracking, cognitive load estimation, emotion recognition, natural language processing, voice-based interaction, and activity recognition. The course is open to beginners and is designed for those who are new to deep learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 67%
Attitudes Surrounding an Imperfect AI Autograder
CHI '21· Explainable AI (XAI) +2
- 67%
On Selective, Mutable and Dialogic XAI: a Review of What Users Say about Different Types of Interactive Explanations
CHI '23· Explainable AI (XAI) +1
- 67%
EXMOS: Explanatory Model Steering through Multifaceted Explanations and Data Configurations
CHI '24· Explainable AI (XAI) +1
- 67%
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CHI '24· Explainable AI (XAI) +1
- 67%
Evaluating the Impact of AI-Generated Visual Explanations on Decision-Making for Image Matching
IUI '25· Explainable AI (XAI) +1
- 60%
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Based on Jaccard similarity of research subtopics & professions (≥60%)