Verfasst von:	Acquaviva, Viviana [VerfasserIn]
Titel:	Machine learning for physics and astronomy
Verf.angabe:	Viviana Acquaviva
Verlagsort:	Princeton ; Oxford
Verlag:	Princeton University Press
E-Jahr:	2023
Jahr:	[2023]
Umfang:	xvi, 259 Seiten
Illustrationen:	Illustrationen, Diagramme
Fussnoten:	Includes bibliographical references and index ; Literaturverzeichnis: Seiten 249-256
ISBN:	978-0-691-20641-7
	978-0-691-20392-8
Abstract:	"A hands-on introduction to machine learning and its applications to the physical sciences. As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task. Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key conceptsIncludes a wealth of review questions and quizzesIdeal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics. Accessible to self-learners with a basic knowledge of linear algebra and calculus. Slides and assessment questions (available only to instructors)"--
URL:	Cover: https://www.dietmardreier.de/annot/426F6F6B446174617C7C393738303639313230333932387C7C434F50.jpg?sq=1
	Inhaltsverzeichnis: http://www.gbv.de/dms/bowker/toc/9780691206417.pdf
Schlagwörter:	(s)Physik    / (s)Astronomie    / (s)Maschinelles Lernen
Sprache:	eng
Bibliogr. Hinweis:	Erscheint auch als : Online-Ausgabe: Acquaviva, Viviana: Machine learning for physics and astronomy. - Princeton : Princeton University Press, 2023
RVK-Notation:	UC 600
Sach-SW:	SCIENCE / Physics / Mathematical & Computational
	COMPUTERS / Data Science / Machine Learning
	Astronomie, Raum und Zeit
	Astronomy, space & time
	Astrophysik
	COM094000
	Maschinelles Lernen
	Physik
	SCIENCE / Astronomy
	SCIENCE / Astrophysics & Space Science
	SCIENCE / Mathematical Physics
	SCIENCE / Research & Methodology
K10plus-PPN:	1848581882