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
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