Abstract
We present the “AI-Atlas” didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24–30) and quantitative exam results (n = 62–113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions—be it on-site, hybrid, or online.
Highlights
Introduction toArtificial Intelligence (AI) (2 weeks)What is intelligence?The concept of a rational agentAI for sci-fi readers: formulating one’s own opinion as a reply to a futuristic essay [6]2
According to our experience and as demonstrated above, the AI-Atlas is highly effective for teaching AI and Machine Learning (ML) principles in an on-site teaching setting
One reason for the effectiveness of the AI-Atlas is that it embodies the general learning settings, as shown in Section 2.3, under which students learn best, adapted to the problems faced by current AI and ML tertiary education
Summary
Introduction toAI (2 weeks)What is (artificial) intelligence?The concept of a rational agentAI for sci-fi readers: formulating one’s own opinion as a reply to a futuristic essay [6]2. AI for sci-fi readers: formulating one’s own opinion as a reply to a futuristic essay [6]. How to find suitable sequences of actions to reach a complex goal?. AI for the game “2048”: controlling a number puzzle game (cf Appendix B). How to represent the world in a way that facilitates reasoning?. Hypothesis space search, inductive bias, computational learning theory, ML as representationoptimization-evaluation. VC dimensions; ML from scratch: implementing linear regression with gradient descent purely from formulae. Feature engineering, making the best of limited data, ensemble learning, debugging ML systems, bias-variance trade-off. & ceiling analysis, SVMs, bagging, boosting, probabilistic graphical models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
