Please use this identifier to cite or link to this item:
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)
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2020_Stadelmann-Wuersch_Teaching-AI-and-machine-learning-ATLAS-concept_TR.pdf582.44 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.