Sparse Estimation with Math and Python
Autor Joe Suzukien Limba Engleză Paperback – 31 oct 2021
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
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Specificații
ISBN-13: 9789811614378
ISBN-10: 9811614377
Pagini: 256
Ilustrații: X, 246 p. 54 illus., 46 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:1st edition 2021
Editura: Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811614377
Pagini: 256
Ilustrații: X, 246 p. 54 illus., 46 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:1st edition 2021
Editura: Springer
Locul publicării:Singapore, Singapore
Cuprins
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.
Notă biografică
Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.
Textul de pe ultima copertă
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 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.
Caracteristici
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