Data Science and Machine Learning: Mathematical and Statistical Methods, Second Edition
暫譯: 資料科學與機器學習：數學與統計方法（第二版）
Botev, Zdravko, Kroese, Dirk P., Taimre, Thomas
- 出版商: CRC
- 出版日期: 2025-11-20
- 售價: $4,150
- 貴賓價: 9.5 折 $3,943
- 語言: 英文
- 頁數: 730
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032488689
- ISBN-13: 9781032488684
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Praise for the first edition:
"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."
- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6
"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely--very useful for readers who wish to understand the rationale and flow of the background knowledge."
- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4
The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.
New in the Second Edition
This expanded edition provides updates across key areas of statistical learning:
- Monte Carlo Methods: A new section introducing the regenerative rejection sampling--a simpler alternative to MCMC.
- Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection.
- Regression: New automatic bandwidth selection for local linear regression.
- Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions.
- Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code.
- Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization--Minimization method for constrained optimization.
Key Features:
- Focuses on mathematical understanding.
- Presentation is self-contained, accessible, and comprehensive.
- Extensive list of exercises and worked-out examples.
- Many concrete algorithms with Python code.
- Full color throughout and extensive indexing.
- A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.
商品描述(中文翻譯)
讚譽第一版：
「在九個簡潔但資訊豐富的章節中，作者提供了一個邏輯結構清晰且穩健的介紹，涵蓋了支撐仍在發展中的人工智慧和數據科學領域的數學和統計方法。」
- Joacim Rocklöv 和 Albert A. Gayle，《國際流行病學期刊》，第49卷，第6期
「這本書清晰且巧妙地組織了演算法。Python 代碼的撰寫方式緊密跟隨演算法，非常有助於希望理解背景知識的推理和流程的讀者。」
- 賴盈如 和 蕭楚興，《生物統計學》，第77卷，第4期
《數據科學與機器學習：數學與統計方法》的目的是提供一本易於理解但又全面的教科書，旨在幫助有興趣深入了解支撐數據科學中豐富多樣的思想和機器學習演算法的數學和統計的學生。
第二版的新內容
這個擴展版在統計學習的關鍵領域提供了更新：
- **蒙地卡羅方法**：新增一節介紹再生拒絕取樣——一種比 MCMC 更簡單的替代方法。
- **無監督學習**：包含兩個多維擴散核密度估計器，以及帶寬擾動匹配方法，用於最佳的數據驅動帶寬選擇。
- **回歸**：針對局部線性回歸的新自動帶寬選擇。
- **特徵選擇與收縮**：新增一章介紹klimax 方法，用於高維度的模型選擇。
- **強化學習**：新增一章涵蓋當代主題，如政策迭代、時間差學習和政策梯度方法，並附有 Python 代碼。
- **附錄**：擴展了線性代數、泛函分析和優化的處理，包括坐標下降法和新穎的主導化-最小化方法，用於約束優化。
主要特點：
- 專注於數學理解。
- 內容自足、易於理解且全面。
- 廣泛的練習題和詳細的範例。
- 許多具體的演算法及其 Python 代碼。
- 全彩印刷並有廣泛的索引。
- 所有定理、定義、方程等的單一連續編號，便於文本搜索。
作者簡介
Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching method, the regenerative rejection sampling method, and the klimax method for feature selection. His contributions to computational statistics and data science have been recognized with honours such as the Christopher Heyde Medal from the Australian Academy of Science and the Gavin Brown Prize from the Australian Mathematical Society.
Dirk P. Kroese, PhD, is an Emeritus Professor in Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.
Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley).
作者簡介(中文翻譯)
Zdravko I. Botev博士是幾種現代統計方法的先驅，包括擴散核密度估計器、稀有事件模擬的廣義分裂方法、帶寬擾動匹配方法、再生拒絕取樣方法，以及klimax方法用於特徵選擇。他在計算統計和數據科學方面的貢獻獲得了多項榮譽，包括澳大利亞科學院的Christopher Heyde Medal和澳大利亞數學學會的Gavin Brown Prize。
Dirk P. Kroese博士是昆士蘭大學數學與統計學的名譽教授。他因在應用概率、數學統計、機器學習和蒙地卡羅方法等領域的重大貢獻而聞名。他已發表超過140篇文章和7本書籍。他是著名的交叉熵（Cross-Entropy, CE）方法的先驅，該方法在全球範圍內被用來幫助解決科學、工程和金融中的困難估計和優化問題。
Thomas Taimre博士是昆士蘭大學數學與統計學的高級講師。他的研究興趣涵蓋應用概率、蒙地卡羅方法、應用物理學以及激光中顯著的普遍自混合效應。他已發表超過100篇文章，擁有一項專利，並且是《蒙地卡羅方法手冊》（Wiley）的共同作者。
