| Detailinformationen |
| Quellcurriculum |
Masterstudium Artificial Intelligence 2020W |
| Ziele |
(*)Visualization has proven to be valuable for explaining and understanding complex machine learning models. This lecture on the topic of Explainable AI (XAI) will give an overview of WHY we want to visualize, WHO uses XAI, WHAT to visualize; HOW to visualize, and WHEN to visualize.
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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).
|
| 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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