Lecturers: Marc Streit, Christian Steinparz, Andreas Hinterreiter
Contact: ai-and-vis-course(at)jku.at

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.

In the practical lab, students will learn how to apply the theoretical concepts and foundations of visualization in the context of artificial intelligence.

Skills

• Interpret different models and understand what insights can be derived from them
• Create elegant and informative data visualizations that help you understand models and communicate their results
• Apply a practical data visualization design workflow to take on any explainable AI and generative VIS challenge
• Evaluate and decide in which situations you need to use state-of-the-art data visualization systems and libraries in the context of AI

Knowledge

• Fundamentals & Explaining Algorithms
• Explaining Through Projections
• Visual Analytics for Deep Learning
• Overview of Explanation Techniques
• Generative AI for visualization
• Selected Recent Work & Case Studies

Criteria for Evaluation

Lecture: Written exam
Lab: Individual and group assignments

Methods

Lecture: Slides combined with case studies and in-class exercises
Lab: Tutorials on various technologies and techniques; presentation and discussion of assignments

Study Material

Study material provided during the course

Langauge

Study material provided during the course

Further Information

The course was formerly known as Explainable AI.