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This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning.
The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage.
In addition, the book has the following features:
- A careful selection of topics ensures rapid progress.
- An opening question at the beginning of each chapter leads the reader through the topic.
- Expositions are rigorous yet based on elementary mathematics.
- More than two hundred exercises help digest the material.
- A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications.
- Numerous suggestions for further reading guide the reader in finding additional information.
This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning.
The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage.
In addition, the book has the following features:
- A careful selection of topics ensures rapid progress.
- An opening question at the beginning of each chapter leads the reader through the topic.
- Expositions are rigorous yet based on elementary mathematics.
- More than two hundred exercises help digest the material.
- A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications.
- Numerous suggestions for further reading guide the reader in finding additional information.
This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
Johannes Lederer is a Professor and the Chair of Mathematics for Data-Driven Methods at the Department of Mathematics, Computer Science, and Natural Sciences, University of Hamburg, Germany. Previously, he was a Professor of Statistics at the Ruhr-University Bochum. He received his PhD in mathematics from the ETH Zürich and subsequently held positions at UC Berkeley, Cornell University, and the University of Washington. He has taught statistical learning and related courses in the US, Belgium, Hong Kong, and Germany to applied and mathematical audiences alike.
Provides a profound yet practical introduction to statistical learning
Interweaves theory with data examples, Python code, and exercises from beginning to end
Features chapter summaries and suggestions for further reading
Part I: Data.- Chapter 1: Fundamentals of Data.- Chapter 2: Exploratory Data Analysis.- Chapter 3: Unsupervised Learning.- Part II: Inferential Data Analyses.- Chapter 4: Linear Regression.- Chapter 5: Logistic Regression.- Chapter 6: Regularization.- Part III: Machine Learning.- Chapter 7: Support-Vector Machines.- Chapter 8: Deep Learning.
| Erscheinungsjahr: | 2025 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xiv
282 S. 13 s/w Illustr. 714 farbige Illustr. 282 p. 727 illus. 714 illus. in color. |
| ISBN-13: | 9783031302756 |
| ISBN-10: | 3031302753 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Lederer, Johannes |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing Springer International Publishing AG |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 241 x 160 x 21 mm |
| Von/Mit: | Johannes Lederer |
| Erscheinungsdatum: | 26.02.2025 |
| Gewicht: | 0,663 kg |
Johannes Lederer is a Professor and the Chair of Mathematics for Data-Driven Methods at the Department of Mathematics, Computer Science, and Natural Sciences, University of Hamburg, Germany. Previously, he was a Professor of Statistics at the Ruhr-University Bochum. He received his PhD in mathematics from the ETH Zürich and subsequently held positions at UC Berkeley, Cornell University, and the University of Washington. He has taught statistical learning and related courses in the US, Belgium, Hong Kong, and Germany to applied and mathematical audiences alike.
Provides a profound yet practical introduction to statistical learning
Interweaves theory with data examples, Python code, and exercises from beginning to end
Features chapter summaries and suggestions for further reading
Part I: Data.- Chapter 1: Fundamentals of Data.- Chapter 2: Exploratory Data Analysis.- Chapter 3: Unsupervised Learning.- Part II: Inferential Data Analyses.- Chapter 4: Linear Regression.- Chapter 5: Logistic Regression.- Chapter 6: Regularization.- Part III: Machine Learning.- Chapter 7: Support-Vector Machines.- Chapter 8: Deep Learning.
| Erscheinungsjahr: | 2025 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
xiv
282 S. 13 s/w Illustr. 714 farbige Illustr. 282 p. 727 illus. 714 illus. in color. |
| ISBN-13: | 9783031302756 |
| ISBN-10: | 3031302753 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Lederer, Johannes |
| Hersteller: |
Springer Nature Switzerland
Springer International Publishing Springer International Publishing AG |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 241 x 160 x 21 mm |
| Von/Mit: | Johannes Lederer |
| Erscheinungsdatum: | 26.02.2025 |
| Gewicht: | 0,663 kg |
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