Joe Suzuki

Sparse Estimation with Math and R (eBook, PDF)

100 Exercises for Building Logic

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 234
• Erscheinungstermin: 4. August 2021
• Englisch
• ISBN-13: 9789811614460
• Artikelnr.: 62376394

Autorenporträt

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

Inhaltsangabe

Chapter 1: Linear Regression.- Chapter 2: Generalized Linear Regression.- Chapter 3: Group Lasso.- Chapter 4: Fused Lasso.- Chapter 5: Graphical Model.- Chapter 6: Matrix Decomposition.- Chapter 7: Multivariate Analysis.

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