Verfasst von:	Winn, John Michael [VerfasserIn]
	Diethe, Thomas [VerfasserIn]
Titel:	Model-based machine learning
Mitwirkende:	Bishop, Christopher M. [MitwirkendeR]
	Guiver, John [MitwirkendeR]
	Zaykov, Yordan [MitwirkendeR]
Verf.angabe:	John Michael Winn with Christopher M. Bishop, Thomas Diethe, John Guiver, Yordan Zaykov
Ausgabe:	First edition
Verlagsort:	Boca Raton ; London ; New York
Verlag:	CRC Press
Jahr:	2024
Umfang:	xvii, 450 Seiten
Illustrationen:	Illustrationen
ISBN:	978-1-4987-5681-5
	978-1-032-55882-0
Abstract:	A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to real world problems. This book tackles this challenge through model-based machine learning, focusing on understanding the assumptions encoded in a machine learning system
DOI:	doi:10.1201/9780429192685
URL:	Cover: https://www.dietmardreier.de/annot/426F6F6B446174617C7C393738313439383735363831357C7C434F50.jpg?sq=2
	DOI: https://doi.org/10.1201/9780429192685
Dokumenttyp:	Fallstudiensammlung
Sprache:	eng
RVK-Notation:	ST 300
Sach-SW:	Automatic control engineering
	BUSINESS & ECONOMICS / Statistics
	COMPUTERS / Machine Theory
	MATHEMATICS / Probability & Statistics / General
	Machine learning
	Maschinelles Lernen
	Probability & statistics
	Regelungstechnik
	Theoretische Informatik
	Wahrscheinlichkeitsrechnung und Statistik
	Ökonometrie und Wirtschaftsstatistik
K10plus-PPN:	186943384X