Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. N° de réf. du vendeur 369570887
Quantité disponible : 4 disponible(s)
Vendeur : Romtrade Corp., STERLING HEIGHTS, MI, Etats-Unis
Etat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. N° de réf. du vendeur ABBB-19424
Quantité disponible : 2 disponible(s)
Vendeur : Books Puddle, Woodside, NY, Etats-Unis
Etat : New. N° de réf. du vendeur 26376474520
Quantité disponible : 4 disponible(s)
Vendeur : Romtrade Corp., STERLING HEIGHTS, MI, Etats-Unis
Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. N° de réf. du vendeur ABBB-259360
Quantité disponible : 2 disponible(s)
Vendeur : SMASS Sellers, IRVING, TX, Etats-Unis
Etat : New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability. N° de réf. du vendeur SNTA-19424
Quantité disponible : 2 disponible(s)
Vendeur : Vedams eBooks (P) Ltd, New Delhi, Inde
Soft cover. Etat : New. This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counter-intuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. In addition, important structural and complexity measures, such as matrix norms and VC-dimension, are discussed. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. This beautifully written text is a scholarly journey through the mathematical and algorithmic foundations of data science. Rigorous but accessible, and with many exercises, it will be a valuable resource for advanced undergraduate and graduate classes. Peter Bartlett, University of California, Berkeley. A lucid account of mathematical ideas that underlie today's data analysis and machine learning methods. I learnt a lot from it, and I am sure it will become an invaluable reference for many students, researchers and faculty around the world. Sanjeev Arora, Princeton University, New Jersey. N° de réf. du vendeur 133977
Quantité disponible : 5 disponible(s)