相關主題
商品描述
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.
商品描述(中文翻譯)
這本教科書介紹了統計學習的基本概念和方法，並使用Python提供了一種獨特的方法，從頭到尾結合了理論、數據示例、軟件代碼和練習，以深入而實用的方式介紹統計學習。
該書分為三個部分：第一部分以概率論、探索性數據分析和無監督學習的框架呈現數據。第二部分介紹了推論數據分析，包括線性和邏輯回歸以及正則化。最後一部分研究了機器學習，重點關注支持向量機和深度學習。每一章都基於一個數據集，可以從該書的主頁下載。
此外，該書具有以下特點：
- 精心選擇的主題確保快速進展。
- 每章開頭的問題引導讀者進入主題。
- 陳述嚴謹，但基於基礎數學。
- 超過兩百個練習幫助消化材料。
- 每章末的簡潔討論部分總結了關鍵概念並突出實際影響。
- 大量進一步閱讀的建議指導讀者尋找額外信息。
這本書適用於希望理解和應用統計學習概念和方法的所有人。典型的讀者包括計算機科學、生物學、心理學、商業和工程等數據密集領域的研究生和高年級本科生，以及準備面試的畢業生。
作者簡介
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.
作者簡介(中文翻譯)
Johannes Lederer是德國鲁爾大學統計學教授。他在ETH蘇黎世理工學院獲得數學博士學位，並先後在加州大學伯克利分校、康奈爾大學和華盛頓大學擔任職位。他曾在美國、比利時、香港和德國教授統計學習和相關課程，面向應用和數學學術界。
