Description

Book Synopsis

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.



Table of Contents
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.

Fundamentals of Data Analytics: With a View to Machine Learning

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    A Paperback by Rudolf Mathar, Gholamreza Alirezaei, Emilio Balda

    15 in stock


      View other formats and editions of Fundamentals of Data Analytics: With a View to Machine Learning by Rudolf Mathar

      Publisher: Springer Nature Switzerland AG
      Publication Date: 17/09/2021
      ISBN13: 9783030568337, 978-3030568337
      ISBN10: 3030568334
      Also in:
      Computing Algorithms and data structures Databases / Data management Mathematics Probability and statistics

      Description

      Book Synopsis

      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.



      Table of Contents
      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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