Book

Foundations of Data Science

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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

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Availability

Detail Information

Series Title

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Call Number

006..312

Publisher

Islamabad : Al Hafiz Traders., 2020

Collation

432

Language

English

ISBN/ISSN

978-1108485067

Classification

006..312

Content Type

-

Media Type

-

Carrier Type

-

Edition

1st

Subject(s)

Computer Science Data Science
1. Computer Science 2. Software Engineering 3. Dat
Data Science Information Technology

Specific Detail Info

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Statement of Responsibility

Avrim Blum , John Hopcroft , Ravindran Kannan

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