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Foundations of Data Science

Foundations of Data Science

£38.99

  • Publication planned for: January 2020
  • availability: Not yet published - available from January 2020
  • format: Hardback
  • isbn: 9781108485067

£ 38.99
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  • 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.

    • Contains over 350 end-of-chapter exercises
    • Includes over 90 figures which illustrate key concepts in the text
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    Reviews & endorsements

    'This beautifully written text is a scholarly journey through the mathematical and algorithmic foundations of data science. Rigorous but accessible, and with many exercises, it will be a valuable resource for advanced undergraduate and graduate classes.' Peter Bartlett, University of California, Berkeley

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    Product details

    • Publication planned for: January 2020
    • format: Hardback
    • isbn: 9781108485067
    • dimensions: 253 x 177 mm
    • availability: Not yet published - available from January 2020
  • 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.

  • Authors

    Avrim Blum, Toyota Technical Institute at Chicago
    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).

    John Hopcroft, Cornell University, New York
    John Hopcroft is a member of the National Academy of Sciences and National Academy of Engineering, and a foreign member of the Chinese Academy of Sciences. He received the Turing Award in 1986, was appointed to the National Science Board in 1992 by President George H. W. Bush, and was presented with the Friendship Award by Premier Li Keqiang for his work in China.

    Ravi Kannan, Microsoft Research, India
    Ravi Kannan is Principal Researcher for Microsoft Research, India. He was the recipient of the Fulkerson Prize in Discrete Mathematics (1991) and the Knuth Prize (ACM) in 2011. He is a distinguished alumnus of the Indian Institute of Technology, Bombay, and his past faculty appointments include Massachusetts Institute of Technology, Carnegie Mellon University, Pennsylvania, Yale University, Connecticut, and the Indian Institute of Science.

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