Abstract
Machine learning (ML) is the essence of the modeling phase of the data science workflow. In this chapter, we focus on the pedagogical challenges of teaching ML to various populations. We first describe the terms white box and black box in the context of ML education (Sect. 13.2). Next, we describe the pedagogical challenge with respect to different learner populations including data science major students as well as non-major students (Sect. 13.3). Then, we present three framework remarks for teaching ML (regarding statistical thinking, interdisciplinary projects, and the application domain knowledge), which, despite not being mentioned frequently in this part of the book, are important to be kept in mind in ML teaching processes (Sect. 13.4). We conclude this chapter by highlighting the importance of ML education in the context of the application domain (Sect. 13.5).
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Notes
- 1.
This section is based on Mike and Hazzan (2022). Machine learning for non-major data science students: A white box approach, special issue on Research on Data Science Education, The Statistics Education Research Journal (SERJ) 21(2), Article 10. Reprint is allowed by SERJ journal’s copyright policy.
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Hazzan, O., Mike, K. (2023). The Pedagogical Challenge of Machine Learning Education. In: Guide to Teaching Data Science. Springer, Cham. https://doi.org/10.1007/978-3-031-24758-3_13
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