This textbook offers a non-mathematical approach to predictive learning, emphasizing methodology and principles. It describes conceptual and philosophical aspects of predictive learning, exploring constructive learning algorithms in a coherent framework. The book includes: concepts, such as complexity control, generalization, and basic modeling approaches;philosophical principles of statistical estimation and machine learning;a presentation of statistical learning theory, a framework for learning algorithms;data-analytic methods; neural network and machine learning methodsnon-standard learning methodologies and their SVM-like mathematical description.This book provides a solid methodologies and practical applications for students and practitioners alike. Exercises range from trivial programming to open-ended research questions. Supplemental material includes a solutions manual, lecture slides, data sets, software implementation, and MATLAB scripts.
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