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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.
Publisher : Cambridge University Press
Publication date : March 12, 2020
Edition : 1st
Language : English
Print length : 432 pages
ISBN-10 : 1108485065
ISBN-13 : 978-1108485067
Item Weight : 2.05 pounds
Dimensions : 7.25 x 1 x 10.25 inches
Best Sellers Rank: #1,418,604 in Books (See Top 100 in Books) #3,200 in AI & Machine Learning #5,194 in Computer Science (Books)
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