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Machine Learning and Explainable AI Lab
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The lab studies machine learning and explainable AI.
AboutAbout the lab
Making AI's decisions interpretable to humans.
Professor Akihiro Yamaguchi's research themes are machine learning, explainable AI (XAI), interpretability, and time-series data mining.
The lab studies methods that make the decisions of AI systems that classify time-series data interpretable to humans.
The professor belongs to the Liaison Strategy Division at the Institute of Mathematics for Industry, Kyushu University.
Key terms
- Explainable AI
- Technology that helps people understand why an AI system made a particular decision. Also called XAI, after the initials of the English name.
- Time-series data mining
- Research on extracting characteristic patterns and rules from data recorded in chronological order.
ResearchResearch themes
The lab studies machine learning, particularly methods for handling time-series data, from the perspective of explainable AI (XAI). Interpretability and time-series data mining are also listed among its research themes.
View More ↗MembersFaculty
- ProfessorAkihiro Yamaguchi
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Unsupervised-Learning” Rakshath / CC BY-SA 4.0 (cropped)
- Related lab (Machine Learning and Explainable AI Lab (Akihiro Yamaguchi)): “Meteorological station 2” Jetam2 / CC BY-SA 4.0 (cropped)
- Related lab (Numerical Analysis and Computational Fluid Dynamics Lab): “Biomechanics-Femur-Fracture-Nail-Internal Fixation-CAD-FEM” Fry72 Karel Frydrýšek, Tomáš Halo, Daniel Čepica / CC BY-SA 4.0 (cropped)
- Related lab (Numerical Analysis and Computational Fluid Dynamics Lab): “AbsoluteErrorNumericalDifferentiationExample” Berland / Public domain (cropped)
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