Übersicht			Auf mobile öffnen

Lieferstatus:	Vorankündigung
Veröffentlichung:	ANGEKÜNDIGT (August 2026)
Genre:	Naturwissensch., Medizin, Technik
	Artificial Intelligence / Computational Intelligence / Data Science / Linear Algebra / machine learning / Maschinelles Lernen / probability spaces / Statistical Estimation
ISBN:	9783032303356
EAN-Code:	9783032303356
Verlag:	Springer International Publishing
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	543
Illustration:	XXX, 543 p. 45 illus., 41 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Keine Bewertung vor Veröffentlichung möglich.

Inhalt:

This book builds a single, coherent pathway from linear algebra to probability and statistical learning-the twin pillars behind modern Data Science, AI, and ML. With equal emphasis on geometry (matrices, spectra, projections) and uncertainty (randomness, estimation, generalization) , it equips readers to derive algorithms from first principles and implement them robustly at scale. Throughout, geometric pictures (projections, angles, spectra) and probabilistic arguments (risk, concentration, generalization) are developed side-by-side. Each concept is motivated by a real ML use case-denoising with PCA, ill-conditioning in regression, choosing regularization via validation curves, or accelerating large least-squares with sketching.