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Foundations of Data Science
출간예정도서
판매가격 45,000원
저자 Avrim Blum, John Hopcroft, Ravi Kannan
도서종류 외국도서
출판사 Cambridge University Press
발행언어 한국어
발행일 2020
페이지수 432
ISBN 9781108485067
배송비결제 주문시 결제
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    Overview

    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.


    Authors

    Avrim BlumToyota 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 HopcroftCornell 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 KannanMicrosoft 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.

    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.

     

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