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A Computational Approach to Statistical Learning

Af: Bryan W. Lewis, Michael Kane, Taylor Arnold Engelsk Hardback

A Computational Approach to Statistical Learning

Af: Bryan W. Lewis, Michael Kane, Taylor Arnold Engelsk Hardback
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A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.

The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

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A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.

The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.

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Produktdetaljer
Sprog: Engelsk
Sider: 376
ISBN-13: 9781138046375
Indbinding: Hardback
Udgave:
ISBN-10: 113804637X
Kategori: Matematik til informatikfag
Udg. Dato: 29 jan 2019
Længde: 27mm
Bredde: 236mm
Højde: 161mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 29 jan 2019
Forfatter(e): Bryan W. Lewis, Michael Kane, Taylor Arnold
Forfatter(e) Bryan W. Lewis, Michael Kane, Taylor Arnold

Kategori Matematik til informatikfag

ISBN-13 9781138046375

Sprog Engelsk

Indbinding Hardback

Sider 376

Udgave

Længde 27mm

Bredde 236mm

Højde 161mm

Udg. Dato 29 jan 2019

Oplagsdato 29 jan 2019

Forlag Taylor & Francis Ltd