| Detailinformationen |
| Quellcurriculum |
Masterstudium Artificial Intelligence 2024W |
| Ziele |
(*)What’s the role of visualization in the age of machine learning? In the algorithm-focused world, visualizations can help understand and explain the input, the inner workings, and the output of machine learning models. In the human-focused world, machine learning can help with tasks such as creating effective visualizations, recommending suitable visualization types, or guiding users toward potentially interesting patterns in large and complex datasets. In this course, we will cover both worlds.
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| Lehrinhalte |
(*)- Introduction and Course Overview
- Fundamentals & Explaining Algorithms
- Explaining Through Projections
- Visual Analytics for Deep Learning
- Overview of Explanation Techniques
- Selected Recent Work & Case Studies
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| Beurteilungskriterien |
(*)Written exam (oral exam in exceptional cases).
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| Lehrmethoden |
(*)Slides combined with case studies and in-class exercises.
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Study material will be provided during the course.
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)The lecture can be combined with an optional practical lab.
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| Äquivalenzen |
(*)in collaboration with 993MLPEEAIU20: UE Explainable AI (1.5 ECTS) equivalent to
993MLPEEAIK19: KV Explainable AI (3 ECTS)
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
993MLPEEAIV20: VL Explainable AI (2020W-2024S)
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