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Helmholtz AI: Diversity in Teaching Machine Learning Supports Democratizing AI

Authors: Cea, D.; Hoffmann, H.; Weiel, M.; Steinbach, P.; Kesselheim, S.;

Helmholtz AI: Diversity in Teaching Machine Learning Supports Democratizing AI

Abstract

“Democratizing AI” – that is the motto for the Helmholtz AI consultants. With our scientific consulting, we enable Helmholtz researchers from all domains to leverage AI for their datasets by providing comprehensive support with AI methods, tools, and software engineering. And this does not only apply to scientists working on their own research projects. We also offer courses, workshops, lectures, and challenges on various AI-related topics. On our poster, you can find an overview of past teaching experiences from the different consultant teams. These include an in-depth introductory course to deep learning using the flipped classroom approach, advanced courses on AutoML and explainable AI with multiple hands-on sessions, data challenges introducing the learners to domain adaptation tasks and making them experiment and search for personal solutions to complex and current problems, and crash courses on AI for a broader and less technical audience. You are curious to learn more? Then drop by our poster and let’s have a chat.

https://www.helmholtz.ai/

Country
Germany
Keywords

machine learning, learning, inclusion, Helmholtz AI, community, diversity, Teaching Machine Learning

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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