The DOI matches its Crossref record; 142 of the 142 references it cites were found in a registry or an index.
Foundations of Data Science
Abstract
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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Cites
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Asymmetric Transitivity Preserving Graph Embedding
Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939751 · Cited by 1,248
Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach
Yang, D., Qu, B., Yang, J., & Cudre-Mauroux, P. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313635 · Cited by 299
Organizing memories for generalization in complementary learning systems
Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01382-9 · Cited by 103
Blum, L., & Blum, M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115934119 · Cited by 70
Metadata
- Authors
- Blum, A., Hopcroft, J., & Kannan, R.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2020-01-17
- OpenAlex
- W2304387544
- Cited by
- 304
- References
- 142
- FWCI
- 18.04
- Type
- book
- Language
- en
- Open access
- closed
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