Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad application of ML and the accelerated pace of its evolution lead to an increasing need for dedicated teaching concepts aimed at making the application of this technology more reliable and responsible. However, teaching ML is a daunting task. Aside from the methodological complexity of ML algorithms, both with respect to theory and implementation, the interdisciplinary and empirical nature of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming and domain expertise. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. These cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.

Constructivism is a knowledge and learning theory that can be applied to artificial intelligence. It argues that learning, knowledge, and understanding are constructive processes that build on prior knowledge. For example, rather than forming a single conception of the world, pieces of information are layered on top of our existing knowledge. When it comes to constructivism in AI, there is the belief that learning or knowledge is created by constructing internal models of the world that are constantly adjusted to fit with new experiences. Constructivism in AI affirms that machine intelligence is best realized by programming machine intelligence systems to behave like infants, with instinctive reflexes, and then gradually learning how to interact with their surroundings.

Edsger Dijkstra's 1988 paper "On the Cruelty of Really Teaching Computer Science" (in plain text form at https://bit.ly/3b6bFto) is one of the most well-cited papers on computer science (CS) education. A growing body of recent research explores the very topic that Dijkstra tried to warn us away from--how we learn and teach computer science with metaphor. According to Google Scholar, Dijkstra's paper has been cited 571 times. In contrast, the most-cited paper in all of the ACM Digital Library papers related to SIGCSE has 412 citations (see data at https://bit.ly/3bae0Ub). Dijkstra's paper has been cited more than any peer-reviewed CS education research.

This is part of a series on Artificial Intelligence. If you are catching it for the first time I'd recommend that you start here where I introduce the idea and provide some instrumental background. In the last article, I talked about the usefulness of thinking about artificial intelligence in its chapters. True, you could start biting this elephant anywhere and anyhow. The phases approach is just my recommended way of understanding, with better clarity, the goals and ultimate intentions of AI.