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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,WahlstrÔøΩm, Niklas,Lindsten, Fredrik,SchÔøΩn, Thomas B

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Cambridge University Press, 6/2/2022 12:00:01 AM. hardcover. Very Good. 0.8268 10.2362 7.1260. Cover and edges may have some wear.
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Details

  • Title Machine Learning: A First Course for Engineers and Scientists
  • Author Lindholm, Andreas,WahlstrÔøΩm, Niklas,Lindsten, Fredrik,SchÔøΩn, Thomas B
  • Binding Hardback
  • Condition Used - Very good
  • Publisher Cambridge University Press
  • Publication date 6/2/2022 12:00:01 AM
  • Features Bibliography, Index
  • Bookseller's Inventory # mon0003867974
  • ISBN 9781108843607
  • Weight 1.92 lbs (0.87 kg)
  • Size 0.8268 10.2362 7.1260
  • Quantity available 1
  • Bookseller catalogues Book

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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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