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  2. Anvendt matematik
  3. Matematisk modellering

Machine Learning

- A First Course for Engineers and Scientists
Af: Andreas Lindholm, Niklas Wahlstrom, Thomas B. Schon, Fredrik Lindsten Engelsk Hardback

Machine Learning

- A First Course for Engineers and Scientists
Af: Andreas Lindholm, Niklas Wahlstrom, Thomas B. Schon, Fredrik Lindsten Engelsk Hardback
Tjek vores konkurrenters priser
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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 350
ISBN-13: 9781108843607
Indbinding: Hardback
Udgave:
ISBN-10: 1108843603
Kategori: Matematisk modellering
Udg. Dato: 31 mar 2022
Længde: 22mm
Bredde: 260mm
Højde: 183mm
Forlag: Cambridge University Press
Oplagsdato: 31 mar 2022
Forfatter(e): Andreas Lindholm, Niklas Wahlstrom, Thomas B. Schon, Fredrik Lindsten
Forfatter(e) Andreas Lindholm, Niklas Wahlstrom, Thomas B. Schon, Fredrik Lindsten

Kategori Matematisk modellering

ISBN-13 9781108843607

Sprog Engelsk

Indbinding Hardback

Sider 350

Udgave

Længde 22mm

Bredde 260mm

Højde 183mm

Udg. Dato 31 mar 2022

Oplagsdato 31 mar 2022

Forlag Cambridge University Press

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