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Erschienen in:
2023 | OriginalPaper | Buchkapitel
14. Core Concepts of Machine Learning
verfasst von : Orit Hazzan, Koby Mike
Erschienen in: Guide to Teaching Data Science
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Abstract
In this chapter, we focus on the teaching of several core concepts that are common to many machine learning (ML) algorithms (such as hyper-parameter tuning) and, as such, are essential learning goals in themselves, regardless of the ML algorithms. Specifically, we discuss types of ML (Sect. 14.2), ML parameters and hyperparameters (Sect. 14.3), model training, validation, and testing (Sect. 14.4), ML performance indicators (Sect. 14.5), bias and variance (Sect. 14.6), model complexity (Sect. 14.7), overfitting and underfitting (Sect. 14.8), loss function optimization and the gradient descent algorithm (Sect. 14.9), and regularization (Sect. 14.10). We conclude this chapter by emphasizing what ML core concepts should be discussed in the context of the application domain (Sect. 14.11).