Fundamentals of Data Analytics
With a View to Machine Learning
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Beschreibung
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.
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.
Comprehensive and in-depth treatment of the mathematical foundations of data science Guiding from general optimization approaches to finding structures in data Provides exercises for each chapter Elaborating the methodological intersection between data science and machine learning Providing material for a three hours per week course on data analytics for graduate students
Autor*in
Rudolf Mathar
Themen in »Fundamentals of Data Analytics«
dimensionality reduction principal component analysis multidimensional scaling classification machine learning data science supervised machine learning dimensionality reduction principal component analysis clustering diffusion maps ISOMAP spectral clustering matrix optimization and approximation support vector machines