Avrim Blum

Foundations of Data Science (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 23. Januar 2020
• Englisch
• ISBN-13: 9781108620321
• Artikelnr.: 66178591

Autorenporträt

Avrim Blum is Chief Academic Officer at Toyota Technical Institute at Chicago and formerly Professor at Carnegie Mellon University, Pennsylvania. He has over 25,000 citations for his work in algorithms and machine learning. He has received the AI Journal Classic Paper Award, ICML/COLT 10-Year Best Paper Award, Sloan Fellowship, NSF NYI award, and Herb Simon Teaching Award, and is a Fellow of the Association for Computing Machinery (ACM).

Inhaltsangabe

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.