This textbook provides a solid mathematical basis for understanding
popular data science algorithms for clustering and classification
and shows that an in-depth understanding of the mathematics
powering these algorithms gives insight into the underlying data.
It presents a step-by-step derivation of these algorithms,
outlining their implementation from scratch in a computationally
sound way. Mathematics of Data Science: A Computational Approach to
Clustering and Classification proposes different ways of
visualizing high-dimensional data to unveil hidden internal
structures, and includes graphical explanations and computed
examples using publicly available data sets in nearly every chapter
to highlight similarities and differences among the algorithms.
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