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210901 ||| eng |
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|a 9789811614460
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| 100 |
1 |
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|a Suzuki, Joe
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| 245 |
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|a Sparse Estimation with Math and R
|h Elektronische Ressource
|b 100 Exercises for Building Logic
|c by Joe Suzuki
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2021, 2021
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| 300 |
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|a X, 234 p. 54 illus., 46 illus. in color
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Machine learning
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| 653 |
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|a Machine Learning
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| 653 |
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|a Statistics
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Artificial intelligence—Data processing
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| 653 |
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|a Statistics
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| 653 |
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|a Data Science
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 856 |
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|u https://doi.org/10.1007/978-981-16-1446-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|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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