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https://doi.org/10.21256/zhaw-20885| Publikationstyp: | Working Paper – Gutachten – Studie |
| Titel: | Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept |
| Autor/-in: | Stadelmann, Thilo Würsch, Christoph |
| et. al: | No |
| DOI: | 10.21256/zhaw-20885 |
| Umfang: | 8 |
| Erscheinungsdatum: | 18-Nov-2020 |
| Verlag / Hrsg. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Verlag / Hrsg. Institution: | Winterthur |
| Sprache: | Englisch |
| Schlagwörter: | Artificial intelligence; Machine learning; Education; Teaching; Didactics; Didactic concept |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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. |
| Weitere Angaben: | Technical Report (Didactic Concept) |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20885 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Stadelmann-Wuersch_Teaching-AI-and-machine-learning-ATLAS-concept_TR.pdf | 582.44 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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