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https://doi.org/10.21256/zhaw-20885| Publication type: | Working paper – expertise – study |
| Title: | Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept |
| Authors: | Stadelmann, Thilo Würsch, Christoph |
| et. al: | No |
| DOI: | 10.21256/zhaw-20885 |
| Extent: | 8 |
| Issue Date: | 18-Nov-2020 |
| Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Publisher / Ed. Institution: | Winterthur |
| Language: | English |
| Subjects: | Artificial intelligence; Machine learning; Education; Teaching; Didactics; Didactic concept |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | Every student seems to have an opinion on AI. This is arguably due to the fact that its assumed topic, “intelligence”, is deemed to be one’s very own possession, and hence an area of every individual’s expertise. To turn this initial motivation into a stable foundation for life-long learning and working, the opposite of ready-made solutions must be made available by an educator. Additionally, the current hype needs to be exposed to thoroughly assess the real potential (for better or worse) of the technology. Hence, students need to be given an ATLAS: a collection of analog maps to the field of AI that (a) give an overview in this highly dynamic and complex environment; that (b) highlight the beauty of certain places therein; that however (c) don’t restrict themselves to advocating only a single path. This paper outlines the concept behind the design and teaching of said “cartographical material” and evaluates it in the context of two curricula: an introduction to AI for undergraduate students of computer science, and an introduction to machine learning in an interdisciplinary masters in engineering programme. It further contributes a model assignment for teaching a fundamental lesson on AI: leveraging the right algorithms pays off way more than leveraging human insight. All course materials including slides, assignments and video lectures, are freely available online. |
| Further description: | Technical Report (Didactic Concept) |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20885 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020_Stadelmann-Wuersch_Teaching-AI-and-machine-learning-ATLAS-concept_TR.pdf | 582.44 kB | Adobe PDF |  View/Open |
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Stadelmann, T., & Würsch, C. (2020). Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-20885
Stadelmann, T. and Würsch, C. (2020) Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-20885.
T. Stadelmann and C. Würsch, “Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept,” ZHAW Zürcher Hochschule für Angewandte Wissenschaften, Winterthur, Nov. 2020. doi: 10.21256/zhaw-20885.
STADELMANN, Thilo und Christoph WÜRSCH, 2020. Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Stadelmann, Thilo, and Christoph Würsch. 2020. “Maps for an Uncertain Future : Teaching AI and Machine Learning Using the ATLAS Concept.” Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-20885.
Stadelmann, Thilo, and Christoph Würsch. Maps for an Uncertain Future : Teaching AI and Machine Learning Using the ATLAS Concept. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 18 Nov. 2020, https://doi.org/10.21256/zhaw-20885.
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