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  • © 2021

Sparse Estimation with Math and R

100 Exercises for Building Logic

Authors:

(view affiliations)
  • 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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eBook
USD 34.99
Price excludes VAT (USA)
  • ISBN: 978-981-16-1446-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 44.99
Price excludes VAT (USA)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-x
  2. Linear Regression

    • Joe Suzuki
    Pages 1-36
  3. Generalized Linear Regression

    • Joe Suzuki
    Pages 37-75
  4. Group Lasso

    • Joe Suzuki
    Pages 77-108
  5. Fused Lasso

    • Joe Suzuki
    Pages 109-144
  6. Graphical Models

    • Joe Suzuki
    Pages 145-178
  7. Matrix Decomposition

    • Joe Suzuki
    Pages 179-200
  8. Multivariate Analysis

    • Joe Suzuki
    Pages 201-231
  9. Back Matter

    Pages 233-234

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 R 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.

Keywords

  • Sparse Estimation
  • Machine Learning
  • linear regression
  • generalized linear lasso
  • group lasso
  • fused lasso
  • graphical models
  • matrix decomposition
  • multivariate analysis

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.

He is the author of a series of textbooks on machine learning
- Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)
- Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762)
- Sparse Estimation with Math and R (This book)
- Sparse Estimation with Math and Python

Bibliographic Information

  • Book Title: Sparse Estimation with Math and R

  • Book Subtitle: 100 Exercises for Building Logic

  • Authors: Joe Suzuki

  • DOI: https://doi.org/10.1007/978-981-16-1446-0

  • 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: 978-981-16-1445-3

  • eBook ISBN: 978-981-16-1446-0

  • Edition Number: 1

  • Number of Pages: X, 234

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

  • Topics: Artificial Intelligence, Machine Learning, Data Science, Statistics

Buying options

eBook
USD 34.99
Price excludes VAT (USA)
  • ISBN: 978-981-16-1446-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 44.99
Price excludes VAT (USA)