Sparse Estimation with Math and Python
100 Exercises for Building Logic
Published on 30. October 2021
X, 246 pages
978-981-16-1438-5 (ISBN)
Description
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.
This book is one of a series of textbooks in machine learning by the same Author. Other titles are:
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
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Singapore
Singapore
Publishing group
Springer Singapore
Product notice
Reflowable
Illustrations
X, 246 p. 54 illus., 46 illus. in color.
File size
4,54 MB
ISBN-13
978-981-16-1438-5 (9789811614385)
DOI
10.1007/978-981-16-1438-5
Schweitzer Classification
Other editions
Additional editions
Book
10/2021
Springer
€40.65
Shipment within 3-4 weeks
Person
Joe Suzuki
is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.
Content
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.
