Inhalt

[ 993MLPEEAIV25 ] VL (*)AI and Visualization

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
1,5 ECTS M2 - Master 2. Jahr Artificial Intelligence Marc Streit 1 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Artificial Intelligence 2025W
Lernergebnisse
Kompetenzen
(*)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.
Fertigkeiten Kenntnisse
(*)
  • 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)
(*)
  • Fundamentals & Explaining Algorithms
  • Explaining Through Projections
  • Visual Analytics for Deep Learning
  • Overview of Explanation Techniques
  • Generative AI for visualization
  • Selected Recent Work & Case Studies
Beurteilungskriterien (*)Written exam
Lehrmethoden (*)Slides combined with case studies and in-class exercises
Abhaltungssprache Englisch
Literatur (*)Study material provided during the course
Lehrinhalte wechselnd? Nein
Sonstige Informationen (*)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.

Äquivalenzen (*)in collaboration with 993MLPEEAIU20: UE Explainable AI (1.5 ECTS) equivalent to
993MLPEEAIK19: KV Explainable AI (3 ECTS)
Frühere Varianten Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
993MLPEEAIV24: VL AI and Visualization (2024W-2025S)
993MLPEEAIV20: VL Explainable AI (2020W-2024S)
Präsenzlehrveranstaltung
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung