TY  - BOOK
AU  - Blum, Avrim
AU  - Hopcroft, John E.
AU  - Kannan, Ravindran
TI  - Foundations of data science
SN  - 9781108485067
U1  - 004 
PY  - 2021///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Quantitative research
KW  - Statistics
KW  - Computer science
N1  - Table of Contents
1. Introduction
2. High-dimensional space
3. Best-fit subspaces and Singular Value Decomposition (SVD)
4. Random walks and Markov chains
5. Machine learning
6. Algorithms for massive data problems: streaming, sketching, and sampling
7. Clustering
8. Random graphs
9. Topic models, non-negative matrix factorization, hidden Markov models, and graphical models
10. Other topics
11. Wavelets
12. Appendix
N2  - 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
ER  -