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.
General
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