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Sparse estimation with math and R = ...

Suzuki, Joe.

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Sparse estimation with math and R = 100 exercises for building logic /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sparse estimation with math and R/ by Joe Suzuki.
其他題名:	100 exercises for building logic /
作者:	Suzuki, Joe.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	x, 234 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis. -
電子資源:	https://doi.org/10.1007/978-981-16-1446-0
ISBN:	9789811614460

Sparse estimation with math and R = 100 exercises for building logic /
Suzuki, Joe.

Sparse estimation with math and R100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Singapore :2021. - x, 234 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811614460

Standard No.: 10.1007/978-981-16-1446-0doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .S89 2021

Dewey Class. No.: 519.535

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505 0 $a 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.
520 $a 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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