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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Mathematical Pictures at a Data Science Exhibition (Hardcover)
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Mathematical Pictures at a Data Science Exhibition (Hardcover)
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This text provides deep and comprehensive coverage of the
mathematical background for data science, including machine
learning, optimal recovery, compressed sensing, optimization, and
neural networks. In the past few decades, heuristic methods adopted
by big tech companies have complemented existing scientific
disciplines to form the new field of Data Science. This text
embarks the readers on an engaging itinerary through the theory
supporting the field. Altogether, twenty-seven lecture-length
chapters with exercises provide all the details necessary for a
solid understanding of key topics in data science. While the book
covers standard material on machine learning and optimization, it
also includes distinctive presentations of topics such as
reproducing kernel Hilbert spaces, spectral clustering, optimal
recovery, compressed sensing, group testing, and applications of
semidefinite programming. Students and data scientists with less
mathematical background will appreciate the appendices that provide
more background on some of the more abstract concepts.
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