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Fundamentals of data analytics = wit...

Mathar, Rudolf.

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Fundamentals of data analytics = with a view to machine learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamentals of data analytics/ by Rudolf Mathar ... [et al.].
其他題名:	with a view to machine learning /
其他作者:	Mathar, Rudolf.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 127 p. :ill. (some col.), digital ;24 cm.
內容註:	1 Introduction -- 2 Prerequisites from Matrix Analysis -- 3 Multivariate Distributions and Moments -- 4 Dimensionality Reduction -- 5 Classification and Clustering -- 6 Support Vector Machines -- 7 Machine Learning -- Index.
Contained By:	Springer Nature eBook
標題:	Statistics - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-56831-3
ISBN:	9783030568313

Fundamentals of data analytics = with a view to machine learning /
Fundamentals of data analyticswith a view to machine learning /[electronic resource] :by Rudolf Mathar ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 127 p. :ill. (some col.), digital ;24 cm.

1 Introduction -- 2 Prerequisites from Matrix Analysis -- 3 Multivariate Distributions and Moments -- 4 Dimensionality Reduction -- 5 Classification and Clustering -- 6 Support Vector Machines -- 7 Machine Learning -- Index.

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

ISBN: 9783030568313

Standard No.: 10.1007/978-3-030-56831-3doiSubjects--Topical Terms:

535534
Statistics
--Data processing.

LC Class. No.: QA276.4

Dewey Class. No.: 519.5

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505 0 $a 1 Introduction -- 2 Prerequisites from Matrix Analysis -- 3 Multivariate Distributions and Moments -- 4 Dimensionality Reduction -- 5 Classification and Clustering -- 6 Support Vector Machines -- 7 Machine Learning -- Index.
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