Introduction to Machine Learning with Applications in Information Security (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
暫譯: 機器學習導論:在資訊安全中的應用 (Chapman & Hall/CRC 機器學習與模式識別)

Mark Stamp

  • 出版商: Chapman and Hall/CRC
  • 出版日期: 2017-09-07
  • 售價: $3,310
  • 貴賓價: 9.5$3,145
  • 語言: 英文
  • 頁數: 364
  • 裝訂: Hardcover
  • ISBN: 1138626783
  • ISBN-13: 9781138626782
  • 相關分類: Machine Learning資訊安全
  • 海外代購書籍(需單獨結帳)

商品描述

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts.

The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis.

Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book.

Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color.

About the Author

Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.

商品描述(中文翻譯)

機器學習導論:資訊安全應用》提供了一個經過課堂測試的機器學習演算法介紹,並透過現實應用加以強化。這本書易於理解,並不證明定理或深入數學理論。其目標是以直觀的方式呈現主題,並提供足夠的細節以澄清基本概念。

本書深入探討核心機器學習主題,包括隱馬可夫模型(Hidden Markov Models)、主成分分析(Principal Component Analysis)、支持向量機(Support Vector Machines)和聚類(Clustering)。此外,還涵蓋了最近鄰(Nearest Neighbors)、神經網絡(Neural Networks)、提升法(Boosting)和AdaBoost、隨機森林(Random Forests)、線性判別分析(Linear Discriminant Analysis)、向量量化(Vector Quantization)、朴素貝葉斯(Naive Bayes)、回歸分析(Regression Analysis)、條件隨機場(Conditional Random Fields)和數據分析(Data Analysis)。

書中的大多數例子來自資訊安全領域,許多機器學習應用特別集中於惡意軟體。所呈現的應用旨在通俗化機器學習技術,提供簡單明瞭的情境。本書中的許多練習需要一些程式設計,並且在某些應用部分假設讀者具備基本的計算概念。然而,任何具備適度程式設計經驗的人都應該能夠輕鬆應對這部分內容。

教學資源,包括PowerPoint簡報、講座視頻和其他相關材料,均可在附屬網站上獲得:http://www.cs.sjsu.edu/~stamp/ML/。為了讀者的方便,書中的圖形也以電子形式提供,並且是彩色的。

關於作者

馬克·斯坦普(Mark Stamp)自2002年以來一直擔任聖荷西州立大學(San Jose State University)的計算機科學教授。在此之前,他在國家安全局(NSA)工作了七年,並在一家矽谷初創公司工作了兩年。他於1992年獲得德克薩斯科技大學(Texas Tech University)的博士學位。他與機器學習的緣分始於1990年代初期,當時他在NSA工作,並在聖荷西州立大學繼續這一熱情,指導了大量碩士生的專案,其中大多數涉及資訊安全和機器學習的結合。