Titlebook: Machine Learning for Engineers; Using data to solve Ryan G. McClarren Textbook 2021 Springer Nature Switzerland AG 2021 supervised learnin

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The Landscape of Machine Learning: Supervised and Unsupervised Learning, Optimization, and Other Tope included to aid in discussions later in the text. The discussion of cross-validation includes k-fold cross-validation, leave-one-out cross-validation, and how to apply cross-validation to time series as well as problems with unknown parameters in the loss function.

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Textbook 2021est addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,? demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally

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Linear Models for Regression and Classification example considering an object in free fall to then use regression to find the acceleration due to gravity. This example then leads to a discussion of least squares regression and various generalizations using logarithmic transforms. The topic of logistic regression is presented as a classification

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