Andreas Lindholm: Machine Learning | Buch

Cover: 9781108843607 | Machine Learning | A First Course for Engineers and Scientists | Buch

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Sprache: Englisch

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Beschreibung

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.

Details

Erscheinungsjahr:	2022
Medium:	Buch
Inhalt:	Gebunden
ISBN-13:	9781108843607
ISBN-10:	1108843603
Sprache:	Englisch
Einband:	Gebunden
Autor:	Lindholm, Andreas Wahlström, Niklas Lindsten, Fredrik Schön, Thomas B.
Auflage:	New ed
Hersteller:	Cambridge University Pr.
Verantwortliche Person für die EU:	preigu GmbH & Co. KG, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Abbildungen:	Worked examples or Exercises
Maße:	258 x 182 x 21 mm
Von/Mit:	Andreas Lindholm (u. a.)
Erscheinungsdatum:	31.03.2022
Gewicht:	0,862 kg