那伽邪無 Tech notes of DeerRIDER

Mathematical Foundations for Data Science


Progress

TODO: research on related subfields and mathematical foundations for Data Science; not planned

Resources

Foundations of Data Science, Jan 21, 2019, Avrim Blum, John Hopcroft, Ravindran Kannan

  • This book covers the theories likely to be useful in the next 40 years, while the majority usage of computer currently is to understand and make usable massive data arising in application.
  • Topics: High-Dimensional Space, Best-Fit Subspaces and Singular Value Decomposition (SVD), Random Graphs, Random Walks and Markov Chains, Learning and VC-dimension, Algorithms for Massive Data Problems, Clustering, Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation, Rankings, Hare System for Voting, Compressed Sensing and Space Vectors.
  • Free PDF Copy: https://www.cs.cornell.edu/jeh/book.pdfFoundations-of-Data-Science

Highlighted Topics and Papers

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