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Sparse Estimation with Math and Python

100 Exercises for Building Logic

Authors: Suzuki, Joe

  • 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

Buy this book

eBook  
  • ISBN 978-981-16-1438-5
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover ca. 51,99 €
price for Spain (gross)
  • Due: November 20, 2021
  • ISBN 978-981-16-1437-8
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

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

About the authors

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

Buy this book

eBook  
  • ISBN 978-981-16-1438-5
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover ca. 51,99 €
price for Spain (gross)
  • Due: November 20, 2021
  • ISBN 978-981-16-1437-8
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules

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Bibliographic Information

Bibliographic Information
Book Title
Sparse Estimation with Math and Python
Book Subtitle
100 Exercises for Building Logic
Authors
Copyright
2021
Publisher
Springer Singapore
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-16-1438-5
Softcover ISBN
978-981-16-1437-8
Edition Number
1
Number of Pages
XX, 200
Number of Illustrations
20 b/w illustrations, 30 illustrations in colour
Topics