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As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning.
Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercisesin each chapter, 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 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning.
Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercisesin each chapter, 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 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
Equips readers with the logic required for machine learning and data science via math and programming
Provides in-depth understanding of R source programs rather than how to use ready-made R packages
Written in an easy-to-follow and self-contained style
| Erscheinungsjahr: | 2020 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xi
217 S. 3 s/w Illustr. 65 farbige Illustr. 217 p. 68 illus. 65 illus. in color. |
| ISBN-13: | 9789811575679 |
| ISBN-10: | 9811575673 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Suzuki, Joe |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore |
| Maße: | 235 x 155 x 13 mm |
| Von/Mit: | Joe Suzuki |
| Erscheinungsdatum: | 20.10.2020 |
| Gewicht: | 0,359 kg |
Equips readers with the logic required for machine learning and data science via math and programming
Provides in-depth understanding of R source programs rather than how to use ready-made R packages
Written in an easy-to-follow and self-contained style
| Erscheinungsjahr: | 2020 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xi
217 S. 3 s/w Illustr. 65 farbige Illustr. 217 p. 68 illus. 65 illus. in color. |
| ISBN-13: | 9789811575679 |
| ISBN-10: | 9811575673 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Suzuki, Joe |
| Auflage: | 1st ed. 2020 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore |
| Maße: | 235 x 155 x 13 mm |
| Von/Mit: | Joe Suzuki |
| Erscheinungsdatum: | 20.10.2020 |
| Gewicht: | 0,359 kg |
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