Data Science and Machine Learning: Mathematical and Statistical Methods

Kroese, Dirk P., Botev, Zdravko, Taimre, Thomas

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商品描述

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey " -Nicholas Hoell, University of Toronto

"This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College

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.

 

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.

The Authors:

Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method--an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.

Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences.

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).

Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.

 

商品描述(中文翻譯)

「這本教科書是一本全面、嚴謹且資訊豐富的作品,介紹了現代機器學習技術背後的數學原理。它涵蓋了所有重要的主題,並且非常適合高年級本科生或初級研究生的數學學生修讀數據科學課程。這本書填補了現有文獻中的一個缺口,它不以廣度為代價而犧牲深度,提供了主要定理和推導的證明,並提供了大量的Python代碼。我只希望在我剛開始學習時有這樣一本書存在。」- 尼古拉斯·霍爾(多倫多大學)

「這是一本寫得很好的書,比許多入門教材更深入地介紹了數據科學方法。文字清晰,內容邏輯性強,從正則化、分類到決策樹,逐步建立起知識體系。與其他可能的競爭者相比,它開創了一個獨特的領域。」- 亞當·洛伊(卡爾頓學院)

《數據科學與機器學習:數學和統計方法》的目的是提供一本易於理解但全面的教科書,旨在幫助學生更好地理解數據科學中豐富的思想和機器學習算法背後的數學和統計學原理。

主要特點:

- 著重於數學理解。
- 內容自成體系,易於理解且全面。
- 大量練習題和實例。
- 提供多種具體的Python代碼。
- 全彩印刷。

作者:

- Dirk P. Kroese,博士,澳大利亞昆士蘭大學數學和統計學教授。他在數學、統計學、數據科學、機器學習和蒙特卡羅方法等領域發表了120多篇文章和五本書。他是著名的交叉熵方法的先驅者,這是一種被世界各地用於解決科學、工程和金融中困難的估計和優化問題的自適應蒙特卡羅技術。

- Zdravko Botev,博士,澳大利亞數學科學研究所數據科學和機器學習講師,任教於澳大利亞新南威爾士大學。他是澳大利亞科學院2018年克里斯托弗·海德獎章的獲得者,以表彰他在數學科學領域的卓越研究成果。

- Thomas Taimre,博士,澳大利亞昆士蘭大學數學和統計學高級講師。他的研究興趣涵蓋應用概率和蒙特卡羅方法,應用物理學以及激光中的非常普遍的自混合效應。他發表了100多篇文章,擁有一項專利,並且是《蒙特卡羅方法手冊》(Wiley)的合著者。

- Radislav Vaisman,博士,澳大利亞昆士蘭大學數學和統計學講師。他的研究興趣涉及應用概率、機器學習和計算機科學的交叉領域。他發表了20多篇文章和兩本書。」

作者簡介

Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method--an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.

Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences.

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).

Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.

 

 

 

 

 

 

 

作者簡介(中文翻譯)

Dirk P. Kroese, PhD,是昆士蘭大學的數學和統計學教授。他在數學、統計學、數據科學、機器學習和蒙特卡羅方法等多個領域發表了超過120篇文章和五本書。他是著名的交叉熵方法的先驅者,這是一種自適應的蒙特卡羅技術,被世界各地用於解決科學、工程和金融中的困難估計和優化問題。

Zdravko Botev,PhD,是澳大利亞數學科學研究所的數據科學和機器學習講師,並在澳大利亞新南威爾士大學悉尼分校任職。他是澳大利亞科學院2018年Christopher Heyde獎章的獲得者,以表彰他在數學科學領域的卓越研究。

Thomas Taimre,PhD,是昆士蘭大學的高級講師,專攻數學和統計學。他的研究興趣涵蓋應用概率和蒙特卡羅方法,應用物理學以及激光中的非常普遍的自混合效應。他發表了100多篇文章,擁有一項專利,並且是《蒙特卡羅方法手冊》(Wiley)的合著者。

Radislav Vaisman,PhD,是昆士蘭大學的數學和統計學講師。他的研究興趣涉及應用概率、機器學習和計算機科學的交叉領域。他發表了20多篇文章和兩本書。