Foundations of Data Science

by Avrim Blum, John Hopcroft, Ravindran Kannan

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.

Genres: Mathematics, Textbooks, Computer Science

432 pages, Hardcover

Published March 12, 2020

Reviews

[B. Mulenga Sindala · Rating 5 out of 5]

Very good technical introduction to Data Science.

[Santiago Ortiz · Rating 4 out of 5]

above my level

[Parisa · Rating 3 out of 5]

Ladies and gentlemen, I want to give you the happy news that I have finally finished reading this book😎🎉

[Abbas Mehrabian · Rating 5 out of 5]

Excellent book. Persian review: http://bi-nahayat.persianblog.ir/post...