Lederer A First Course in Statistical Learning

With Data Examples and Python Code
Erscheinungsjahr 2025
ISBN: 978-3-031-30276-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 282 Seiten, eBook

Reihe: Statistics and Computing

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Lederer, Johannes

Inhaltsverzeichnis

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.

Über Autor(innen)

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.