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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
About the Author:
Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research interests, strongly rooted in numerical analysis, include inverse problems, uncertainty quantification, and mathematical modeling, with a specific focus on human metabolism and the brain. She is a member of SIAM and the International Society for Cerebral Blood Flow and Metabolism.
Erkki Somersalo is a professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University, with a background in analysis and partial differential equations. His research interests include computational inverse problems, with an emphasis on Bayesian methods, and applications to a wide range of areas, particularly to medical imaging. He is a founding member of the Finnish Inverse Problems Society and a member of the Finnish Academy of Sciences and Letters and SIAM. He is also a fellow of the Institute of Physics, UK.
Title: Mathematics of Data Science: A Computational...
Publisher: SIAM - Society for Industrial and Applied Mathematics
Publication Date: 2021
Binding: Soft cover
Condition: Good
Seller: Revaluation Books, Exeter, United Kingdom
Paperback / Softback. Condition: Brand New. 189 pages. 10.94x8.43x0.91 inches. In Stock. Seller Inventory # __1611976367
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Seller: Rarewaves.com UK, London, United Kingdom
Paperback. Condition: New. 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 Classificationproposes different ways of visualizing high-dimensional data to unveil hidden internal structures, andincludes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms. Seller Inventory # LU-9781611976366
Quantity: 1 available
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. Seller Inventory # B9781611976366
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Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # FW-9781611976366
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Seller: moluna, Greven, Germany
Condition: New. 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. Seller Inventory # 1607135719
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Paperback. Condition: New. 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 Classificationproposes different ways of visualizing high-dimensional data to unveil hidden internal structures, andincludes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms. Seller Inventory # LU-9781611976366
Quantity: 1 available
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Neuware. Seller Inventory # 9781611976366
Seller: Zoom Books East, Glendale Heights, IL, U.S.A.
Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service. Seller Inventory # ZEV.1611976367.VG