Probability and Statistics for Machine Learning: A Textbook

Aggarwal, Charu C.

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商品描述

This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:

1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5.

2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters.

3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations.

The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners.

商品描述(中文翻譯)

本書從機器學習的角度介紹了概率和統計學。本書的章節分為三個類別:

1. 概率和統計的基礎:這些章節著重介紹概率和統計的基礎知識,涵蓋了這些主題的關鍵原則。第1章概述了概率和統計領域以及其與機器學習的關係。第2至5章介紹了概率和統計的基本原理。

2. 從概率到機器學習:許多機器學習應用使用概率模型來解決問題,並通過數據驅動的方式學習模型參數。第6至9章探討了如何將概率和統計中的不同模型應用於機器學習。從數據到概率的最重要工具可能是最大似然估計,這是從機器學習的角度來看的基礎概念。這個概念在這些章節中反復探討。

3. 進階主題:第10章介紹了離散狀態馬爾可夫過程。它探討了概率和統計在時間和序列設置中的應用,儘管應用範圍擴展到更複雜的情境,如圖形數據。第11章涵蓋了一些概率不等式和近似方法。

本書的寫作風格同時促進了對概率和統計的學習,以及對機器學習應用建模的概率觀點。書中包含200多個實例,以闡明關鍵概念。章節內和章節結尾都包含練習題。本書面向廣泛的讀者,包括研究生、研究人員和從業人員。

作者簡介

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 20 books, including textbooks on linear algebra, machine learning, neural networks, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several awards, including the EDBT Test-of-Time Award (2014), the ACM SIGKDD Innovation Award (2019), the IEEE ICDM Research Contributions Award (2015), and the IIT Kanpur Distinguished Alumnus Award (2023).He is also a recipient of the W. Wallace McDowell Award, the highest award given solely by the IEEE Computer Society across the field of computer science. He has served as an editor-in-chief of ACM Books and is currently serving as an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He is a fellow of the SIAM, ACM, and the IEEE, for"contributions to knowledge discovery and data mining algorithms."

作者簡介(中文翻譯)

Charu C. Aggarwal是紐約約克鎮IBM T. J. Watson研究中心的傑出研究員。他於1993年在印度坎普爾的印度理工學院獲得計算機科學的學士學位,並於1996年在麻省理工學院獲得運籌學的博士學位。他在被審查的會議和期刊上發表了400多篇論文,並申請或獲得了80多項專利。他是20本書的作者或編輯,包括線性代數、機器學習、神經網絡和異常值分析的教科書。由於他的專利具有商業價值,他曾三次被IBM指定為大師發明家。他獲得了多個獎項,包括EDBT Test-of-Time獎(2014年)、ACM SIGKDD創新獎(2019年)、IEEE ICDM研究貢獻獎(2015年)和IIT坎普爾傑出校友獎(2023年)。他還獲得了W. Wallace McDowell獎,這是IEEE計算機學會在計算機科學領域中唯一頒發的最高獎項。他曾擔任ACM Books的主編,目前擔任ACM Transactions on Knowledge Discovery from Data的主編。他是SIAM、ACM和IEEE的會士,以表彰他在知識發現和數據挖掘算法方面的貢獻。