| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2026W |
| Learning Outcomes |
Competences |
| Students have obtained a comprehensive and practical understanding of how to combine visualization and artificial intelligence. On the one hand, they know how to leverage visualization to understand and explain the input, the inner workings, and the output of machine learning models. On the other hand, they know how to leverage machine learning to create effective visualizations, recommend suitable visualization types, or guide users towards potentially interesting patterns in complex datasets.
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| Skills |
Knowledge |
- Interpret different models and understand what insights can be derived from them (k2)
- Create elegant and informative data visualizations that help you understand models and communicate their results (k3, k6)
- Apply a practical data visualization design workflow to take on any explainable AI and generative VIS challenge (k3, k6)
- Evaluate and decide in which situations you need to use state-of-the-art data visualization systems and libraries in the context of AI (k5)
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- Fundamentals & Explaining Algorithms
- Explaining Through Projections
- Visual Analytics for Deep Learning
- Overview of Explanation Techniques
- Generative AI for visualization
- Selected Recent Work & Case Studies
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| Criteria for evaluation |
eExam (Moodle test), physical presence is required in Linz / Vienna / Bregenz or oral exam in Linz
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| Methods |
Slides combined with case studies and in-class exercises
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| Language |
English |
| Study material |
Study material provided during the course
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| Changing subject? |
No |
| Further information |
In addition to the lecture, students can take an optional practical lab to learn how to combine AI methods with visualization theory.
The course was formerly known as Explainable AI.
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| Corresponding lecture |
in collaboration with 993MLPEEAIU20: UE Explainable AI (1.5 ECTS) equivalent to
993MLPEEAIK19: KV Explainable AI (3 ECTS)
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
993MLPEEAIV24: VL AI and Visualization (2024W-2025S)
993MLPEEAIV20: VL Explainable AI (2020W-2024S)
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