Foundations of Data Science

ISBN-13: 9781108485067 / Angielski / Twarda / 2020 / 432 str.

Avrim Blum; John Hopcroft; Ravindran Kannan

Opis

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.

Recenzje

'One plausible measure of [Foundations of Data Science's] impact is the book's own citation metrics. Semantic Scholar (https://www.semanticscholar.org) reports 81 citations with 42 citations related to background or methods; [Foundations of Data Science] appears to be on course to becoming influential.' M. Mounts, Choice

Spis treści

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.