Cookie-Einstellungen
Diese Website benutzt Cookies, die für den technischen Betrieb der Website erforderlich sind und stets gesetzt werden. Andere Cookies, die den Komfort bei Benutzung dieser Website erhöhen, der Direktwerbung dienen oder die Interaktion mit anderen Websites und sozialen Netzwerken vereinfachen sollen, werden nur mit Ihrer Zustimmung gesetzt.
Konfiguration
Technisch erforderlich
Diese Cookies sind für die Grundfunktionen des Shops notwendig.
"Alle Cookies ablehnen" Cookie
"Alle Cookies annehmen" Cookie
Ausgewählter Shop
CSRF-Token
Cookie-Einstellungen
Individuelle Preise
Kundenspezifisches Caching
PayPal-Zahlungen
Session
Währungswechsel
Komfortfunktionen
Diese Cookies werden genutzt um das Einkaufserlebnis noch ansprechender zu gestalten, beispielsweise für die Wiedererkennung des Besuchers.
Merkzettel
Statistik & Tracking
Endgeräteerkennung
Google Analytics
Partnerprogramm
Fundamentals of Pattern Recognition and Machine Learning
53,49 €
inkl. MwSt. zzgl. Versandkosten
Versandkostenfreie Lieferung!
Sofort versandfertig, Lieferfrist: ca. 1-3 Tage
- Artikel-Nr.: 9783030276584
- EAN: 9783030276584
- Volumen: '0'
- Breite: '0'
Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester... mehr
Produktinformationen "Fundamentals of Pattern Recognition and Machine Learning"
Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
Einband/Bindung: | Taschenbuch |
Sprache: | Englisch |
Seitenzahl: | 376 |
Erscheinungsjahr: | 2021 |
Autor: | Ulisses Braga-Neto |
Weiterführende Links zu "Fundamentals of Pattern Recognition and Machine Learning"
Zuletzt angesehen