Sparse Estimation with Math and Python - 100 Exercises for Building Logic

von Joe Suzuki
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Joe Suzuki Sparse Estimation with Math and Python - 100 Exercises for Building Logic
Joe Suzuki - Sparse Estimation with Math and Python - 100 Exercises for Building Logic

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Beschreibung

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

Mitwirkende

Autor:
Joe Suzuki

Weitere Informationen

Anmerkung Illustrationen:
X, 246 p. 54 illus., 46 illus. in color.
Inhaltsverzeichnis:

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.

Bemerkungen:
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

Medientyp:
Taschenbuch
Verlag:
Springer Singapore
Biografie:
Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.
Sprache:
Englisch
Auflage:
1st ed. 2021
Seitenanzahl:
246

Stammdaten

Produkttyp:
Buch Gebunden
Veröffentlichungsdatum:
30. November 2021
Verpackungsabmessungen:
0.235 x 0.154 x 0.012 m; 0.39 kg
GTIN:
09789811614378
DUIN:
9LOQIC4Q3LA
CHF 38.35
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