A First Course in Statistical Learning, 1st ed. 2024 With Data Examples and Python Code Statistics and Computing Series

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.

Johannes Lederer is a Professor of Statistics at the Ruhr-University Bochum, Germany. 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