Abstract
This chapter presents basic concepts and definitions about machine learning, including data representation, dataset, hypothesis space, inductive bias, and various learning tasks and learning schemes. Moreover, we will also discuss density estimation, ground truth, and underlying data distribution.
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References
Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Huang, X., Jin, G., Ruan, W. (2023). Machine Learning Basics. In: Machine Learning Safety. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-6814-3_1
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DOI: https://doi.org/10.1007/978-981-19-6814-3_1
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