Abstract
This chapter presents the principles of machine learning (ML) as the support for shallow and especially deep learning procedures’ software implementations. The main difference between ML and artificial intelligence (AI) is simply given from their deep learning methodologies. Although ML depends on the mathematical procedures mentioned in Chaps. 3 and 4, AI is stated to imitate and mimic human brain functions (Chaps. 6 and 7). Elements and aspects of human thinking for ML are described as shallow and deep learning skills and abilities. Cases of data reliability, model, no data, lack of data, measurement and sampling errors, misclassification, and missing data are discussed, and the relevant uncertainty reduction methodologies are presented. A roadmap is also provided for data reliability adjustment followed by model output prediction validation. Several loss function types are explained by their ease in finding the best (least error) mathematical or AI models. Unsupervised, supervised, and reinforced learning alternatives are explained comparatively. Both bivalent logic k-means and fuzzy logic c-means classification methodologies are explained with their philosophical, logical, and mathematical backgrounds.
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Appendix: Required Software for Data Reliability Analysis
Appendix: Required Software for Data Reliability Analysis
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Şen, Z. (2023). Machine Learning. In: Shallow and Deep Learning Principles. Springer, Cham. https://doi.org/10.1007/978-3-031-29555-3_8
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