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Ajouter au panierSoft 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.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Speedyhen, London, Royaume-Uni
EUR 53,62
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 60,58
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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EUR 52,93
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Ajouter au panierEtat : Very Good. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 63,23
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 56,92
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierEtat : New. This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and resear.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 60,57
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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EUR 69,56
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally.
Edité par Cambridge University Press CUP, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 79,22
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Ajouter au panierEtat : New.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 68,66
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Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
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Ajouter au panierEtat : New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 82,96
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Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : CitiRetail, Stevenage, Royaume-Uni
EUR 67,52
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Ajouter au panierHardcover. Etat : new. Hardcover. 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 counterintuitive 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. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
EUR 87,31
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Ajouter au panierHardcover. Etat : Brand New. 424 pages. 10.25x7.25x1.00 inches. In Stock.
Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
EUR 88,53
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Ajouter au panierHardcover. Etat : new. Hardcover. 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 counterintuitive 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. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 91,22
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Ajouter au panierHardcover. Etat : New. New. book.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 56,10
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Ajouter au panierEtat : New.
Edité par Cambridge University Press, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : HPB-Red, Dallas, TX, Etats-Unis
EUR 38,29
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Ajouter au panierhardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Edité par Cambridge University Press, Cambridge, 2020
ISBN 10 : 1108485065 ISBN 13 : 9781108485067
Langue: anglais
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
EUR 69,06
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierHardcover. Etat : new. Hardcover. 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 counterintuitive 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. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 62,25
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Ajouter au panierHardcover. Etat : Brand New. 424 pages. 10.25x7.25x1.00 inches. In Stock. This item is printed on demand.