Pradeep Singh,
Balasubramanian Raman
Foundations of Machine Learning and AI
Geometry, Probability and Optimization
Gebonden Engels 2026 9783032303356Levertijd ± 9 werkdagen
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Samenvatting
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.
Specificaties
ISBN13:9783032303356
Taal:Engels
Bindwijze:Gebonden
Aantal pagina's:558
Uitgever:Springer Nature Switzerland
Serie:Studies in Big Data
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
Mathematical Language & Notation.- Geometry of Rn: Norms, Inner Products, Projections.- Linear Maps & Matrix Algebra.- Spectral Theory, SVD, and Principal Components.- Numerical Linear Algebra for Data.
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