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Sparse Estimation with Math and Python

100 Exercises for Building Logic

  • Textbook
  • © 2021

Overview

Authors:
  1. Joe Suzuki

    1. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan

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  • Equips readers with the logic required for machine learning and data science
  • Provides in-depth understanding of source programs
  • Written in an easy-to-follow and self-contained style

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-x
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  2. Linear Regression

    • Joe Suzuki
    Pages 1-38

  3. Generalized Linear Regression

    • Joe Suzuki
    Pages 39-80

  4. Group Lasso

    • Joe Suzuki
    Pages 81-114

  5. Fused Lasso

    • Joe Suzuki
    Pages 115-154

  6. Graphical Models

    • Joe Suzuki
    Pages 155-190

  7. Matrix Decomposition

    • Joe Suzuki
    Pages 191-212

  8. Multivariate Analysis

    • Joe Suzuki
    Pages 213-243

  9. Back Matter

    Pages 245-246
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Keywords

About this book

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. 

Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.

This book is one of a series of textbooks in machine learning by the same Author. Other titles are: 
  • Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)
  • Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762)
  • Sparse Estimation with Math and R




Authors and Affiliations

  • Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan

    Joe Suzuki

About the author

Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.



Bibliographic Information

  • Book Title: Sparse Estimation with Math and Python

  • Book Subtitle: 100 Exercises for Building Logic

  • Authors: Joe Suzuki

  • DOI: https://doi.org/10.1007/978-981-16-1438-5

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

  • Softcover ISBN: 978-981-16-1437-8Published: 31 October 2021

  • eBook ISBN: 978-981-16-1438-5Published: 30 October 2021

  • Edition Number: 1

  • Number of Pages: X, 246

  • Number of Illustrations: 8 b/w illustrations, 46 illustrations in colour

  • Topics: Artificial Intelligence, Machine Learning, Data Structures and Information Theory, Statistics, general

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