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Sparse Estimation with Math and Python : 100 Exercises for Building Logic / by Joe Suzuki.
- Format:
- Book
- Author/Creator:
- Suzuki, Joe, Author.
- Series:
- Computer Science (SpringerNature-11645)
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Machine learning.
- Artificial intelligence-Data processing.
- Statistics.
- Artificial Intelligence.
- Machine Learning.
- Data Science.
- Local Subjects:
- Artificial Intelligence.
- Machine Learning.
- Data Science.
- Statistics.
- Physical Description:
- 1 online resource (X, 246 pages) : 54 illustrations, 46 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-1438-5
- 9789811614385
- Access Restriction:
- Restricted for use by site license.
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