Data Science and Machine Learning: Mathematical and Statistical Methods
暫譯: 資料科學與機器學習：數學與統計方法

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

Data Science and Machine Learning: Mathematical and Statistical Methods

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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)

「這本教科書是一部全面、嚴謹且資訊豐富的作品，呈現了現代機器學習技術背後的數學。它涵蓋了所有重要的主題：所選的主題是最新的，並且非常適合高年級本科生或早期研究生的數據科學課程。這本書填補了現有文獻中的一個迫切需要的空白，沒有為了廣度而犧牲深度，展示了主要定理的證明及其後續推導，並提供了大量的 Python 代碼。我只希望在我開始我的學習旅程時，能有這樣的書籍存在。」 -Nicholas Hoell, 多倫多大學

「這是一本寫得很好的書，比許多入門書籍更深入探討數據科學方法。文筆清晰，邏輯上逐步建立了正則化、分類和決策樹。與其可能的競爭對手相比，它開創了一個獨特的利基。」 -Adam Loy, 卡爾頓學院

《數據科學與機器學習：數學與統計方法》的目的是提供一本可接觸且全面的教科書，旨在幫助有興趣深入了解支撐數據科學中各種想法和機器學習算法的數學和統計的學生。

主要特點：


  • 專注於數學理解。

  • 內容自成體系，易於理解且全面。

  • 大量練習題和詳細示例。

  • 許多具體算法及其 Python 代碼。

  • 全書全彩印刷。

作者：

Dirk P. Kroese，博士，是昆士蘭大學的數學與統計學教授。他在數學、統計、數據科學、機器學習和蒙地卡羅方法等廣泛領域發表了超過120篇文章和五本書籍。他是著名的交叉熵方法的先驅——這是一種自適應的蒙地卡羅技術，正在全球範圍內用於解決科學、工程和金融中的困難估計和優化問題。

Zdravko Botev，博士，是澳大利亞數學科學研究所的數據科學與機器學習講師，任職於澳大利亞新南威爾士大學。他是2018年澳大利亞科學院克里斯托弗·海德獎章的獲得者，以表彰他在數學科學領域的卓越研究。

Thomas Taimre，博士，是昆士蘭大學的高級講師，專注於數學與統計學。他的研究興趣涵蓋應用概率、蒙地卡羅方法、應用物理以及激光中的顯著普遍自混合效應。他發表了超過100篇文章，擁有一項專利，並共同編著了《蒙地卡羅方法手冊》（Wiley）。

Radislav Vaisman，博士，是昆士蘭大學的數學與統計學講師。他的研究興趣位於應用概率、機器學習和計算機科學的交集上。他發表了超過20篇文章和兩本書籍。

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與統計學教授。他在數學、統計學、數據科學、機器學習和蒙地卡羅方法等廣泛領域發表了超過120篇文章和五本書籍。他是著名的交叉熵方法的先驅，這是一種自適應的蒙地卡羅技術，正在全球範圍內用於解決科學、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與機器學習講師，任職於澳大利亞悉尼的新南威爾士大學。他因在數學科學領域的卓越研究而獲得2018年澳大利亞科學院的克里斯多福·海德獎章。

Thomas Taimre, PhD 是昆士蘭大學的數學與統計學高級講師。

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的顯著普遍自混合效應。他發表了超過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。

Radislav Vaisman, PhD，是昆士蘭大學的數學與統計學講師。他的研究興趣位於應用概率、機器學習和計算機科學的交叉點上。他發表了超過20篇文章和兩本書籍。

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