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Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah. He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing. His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields. He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science. His research is supported by numerous NSF awards including an NSF Career Award.
Provides accessible, simplified introduction to core mathematical language and concepts
Integrates examples of key concepts through geometric illustrations and Python coding
Addresses topics in locality sensitive hashing, graph-structured data, and big data processing as well as basic linear algebra
Includes perspectives on ethics in data
Erscheinungsjahr: | 2021 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Reihe: | Springer Series in the Data Sciences |
Inhalt: |
xvii
287 S. 1 s/w Illustr. 108 farbige Illustr. 287 p. 109 illus. 108 illus. in color. |
ISBN-13: | 9783030623401 |
ISBN-10: | 3030623408 |
Sprache: | Englisch |
Herstellernummer: | 978-3-030-62340-1 |
Ausstattung / Beilage: | HC runder Rücken kaschiert |
Einband: | Gebunden |
Autor: | Phillips, Jeff M. |
Auflage: | 1st ed. 2021 |
Hersteller: |
Springer International Publishing
Springer Series in the Data Sciences |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 241 x 160 x 22 mm |
Von/Mit: | Jeff M. Phillips |
Erscheinungsdatum: | 30.03.2021 |
Gewicht: | 0,685 kg |
Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah. He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing. His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields. He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science. His research is supported by numerous NSF awards including an NSF Career Award.
Provides accessible, simplified introduction to core mathematical language and concepts
Integrates examples of key concepts through geometric illustrations and Python coding
Addresses topics in locality sensitive hashing, graph-structured data, and big data processing as well as basic linear algebra
Includes perspectives on ethics in data
Erscheinungsjahr: | 2021 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Reihe: | Springer Series in the Data Sciences |
Inhalt: |
xvii
287 S. 1 s/w Illustr. 108 farbige Illustr. 287 p. 109 illus. 108 illus. in color. |
ISBN-13: | 9783030623401 |
ISBN-10: | 3030623408 |
Sprache: | Englisch |
Herstellernummer: | 978-3-030-62340-1 |
Ausstattung / Beilage: | HC runder Rücken kaschiert |
Einband: | Gebunden |
Autor: | Phillips, Jeff M. |
Auflage: | 1st ed. 2021 |
Hersteller: |
Springer International Publishing
Springer Series in the Data Sciences |
Verantwortliche Person für die EU: | Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de |
Maße: | 241 x 160 x 22 mm |
Von/Mit: | Jeff M. Phillips |
Erscheinungsdatum: | 30.03.2021 |
Gewicht: | 0,685 kg |