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Machine Learning: A First Course for Engineers and Scientists

Machine Learning: A First Course for Engineers and Scientists

Machine Learning: A First Course for Engineers and Scientists
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Machine Learning: A First Course for Engineers and Scientists Hardback - 2022

by Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B

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Description

Cambridge University Press, 2022. First Edition. Hardcover. Like New. 7x0x10. Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs or very mild bumps. Hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price.
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Details

  • Title Machine Learning: A First Course for Engineers and Scientists
  • Author Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B
  • Binding Hardback
  • Edition First Edition
  • Condition New
  • Publisher Cambridge University Press
  • Publication date 2022
  • Features Bibliography, Index
  • Bookseller's Inventory # 203938
  • ISBN 9781108843607
  • Weight 1.94 lbs (0.88 kg)
  • Size 7x0x10
  • Quantity available 1

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Reader reviews for Machine Learning: A First Course for Engineers and Scientists

From the publisher

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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