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Linear Algebra Tool Data (2nd Ed)

by Simovici Dan a

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Linear algebra for data applications

$239.22 USD
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Hardcover New English

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AlbakiReads Editorial

Linear algebra for data applications

This reference connects foundational and advanced linear algebra with methods used in data mining and machine learning. Its coverage ranges from spectral theory and singular values to matrix decompositions, tensors, and multidimensional arrays, then turns to applications including k-means clustering, biplots, least-squares approximation, and dimensionality reduction. More than 600 exercises, supplements, selected solutions, and MATLAB applications give the volume a substantial problem-solving component. The second edition is positioned as both a technical foundation and a resource for exploring how linear algebra concepts are used across data-focused fields.

Good fit for readers who enjoy

  • Graduate students building a foundation for data mining or machine learning
  • Researchers and professionals in AI, pattern recognition, image analysis, or databases
  • Readers seeking extensive exercises and MATLAB applications alongside reference material

Themes

Linear algebraData mining and machine learningMatrix and tensor methodsDimensionality reductionComputational exercises

Based on publisher information and book metadata.

Book overview

This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.

 

Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).

 

The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.

 

The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.

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