Machine learning a first course for engineers and scientists

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This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

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Taal: en

Bindwijze: Hardcover

Oorspronkelijke releasedatum: 31 maart 2022

Aantal pagina's: 350

Illustraties: Met illustraties

Hoofdauteur: Andreas Lindholm

Tweede Auteur: Niklas Wahlstroem

Hoofduitgeverij: Cambridge University Press

Editie: New edition

Product breedte: 182 mm

Product hoogte: 20 mm

Product lengte: 259 mm

Studieboek: Nee

Verpakking breedte: 182 mm

Verpakking hoogte: 20 mm

Verpakking lengte: 259 mm

Verpakkingsgewicht: 880 g

EAN: 9781108843607

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