by Roman Egger

Salzburg University of Applied Sciences, Innovation and Management in Tourism

In the last few years, a large hype around the topic of Machine Learning (ML) has emerged; this can be justified thanks to various ML approaches having recently undergone rapid developments and being applied to a wide array of tasks, both in practice as well as in science. Nevertheless, ML approaches still seem to be the exception rather than the rule in tourism literature, most likely due to a sense of hesitation towards understanding and engaging with the technical and theoretical aspects of the topic. As such, this chapter aims to provide an overview and outline the field of Machine Learning by defining basic terms, introducing the most important ML paradigms, and naming relevant algorithms. It also mentions the typical process/procedure, limitations, and challenges of ML as well as developments towards Auto-ML. Thus, a basic understanding of the topic of ML and its importance for tourism is provided here, also serving as a basis for other chapters in this book in which individual algorithms will be presented in more detail.