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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Mark Stamp |

ISBN: | 9781315213262 1315213265 9781351818070 1351818074 |

OCLC Number: | 993988933 |

Description: | 1 online resource : text file, PDF |

Contents: | Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; About the Author; Acknowledgments; 1: Introduction; 1.1 What Is Machine Learning?; 1.2 About This Book; 1.3 Necessary Background; 1.4 A Few Too Many Notes; I: Tools of the Trade; 2: A Revealing Introduction to Hidden Markov Models; 2.1 Introduction and Background; 2.2 A Simple Example; 2.3 Notation; 2.4 The Three Problems; 2.4.1 HMM Problem 1; 2.4.2 HMM Problem 2; 2.4.3 HMM Problem 3; 2.4.4 Discussion; 2.5 The Three Solutions; 2.5.1 Solution to HMM Problem 1; 2.5.2 Solution to HMM Problem 2. 2.5.3 Solution to HMM Problem 32.6 Dynamic Programming; 2.7 Scaling; 2.8 All Together Now; 2.9 The Bottom Line; 2.10 Problems; 3: A Full Frontal View of Profile Hidden Markov Models; 3.1 Introduction; 3.2 Overview and Notation; 3.3 Pairwise Alignment; 3.4 Multiple Sequence Alignment; 3.5 PHMM from MSA; 3.6 Scoring; 3.7 The Bottom Line; 3.8 Problems; 4: Principal Components of Principal Component Analysis; 4.1 Introduction; 4.2 Background; 4.2.1 A Brief Review of Linear Algebra; 4.2.2 Geometric View of Eigenvectors; 4.2.3 Covariance Matrix; 4.3 Principal Component Analysis; 4.4 SVD Basics. 4.5 All Together Now4.5.1 Training Phase; 4.5.2 Scoring Phase; 4.6 A Numerical Example; 4.7 The Bottom Line; 4.8 Problems; 5: A Reassuring Introduction to Support Vector Machines; 5.1 Introduction; 5.2 Constrained Optimization; 5.2.1 Lagrange Multipliers; 5.2.2 Lagrangian Duality; 5.3 A Closer Look at SVM; 5.3.1 Training and Scoring; 5.3.2 Scoring Revisited; 5.3.3 Support Vectors; 5.3.4 Training and Scoring Re-revisited; 5.3.5 The Kernel Trick; 5.4 All Together Now; 5.5 A Note on Quadratic Programming; 5.6 The Bottom Line; 5.7 Problems; 6: A Comprehensible Collection of Clustering Concepts. 6.1 Introduction6.2 Overview and Background; 6.3 K-Means; 6.4 Measuring Cluster Quality; 6.4.1 Internal Validation; 6.4.2 External Validation; 6.4.3 Visualizing Clusters; 6.5 EM Clustering; 6.5.1 Maximum Likelihood Estimator; 6.5.2 An Easy EM Example; 6.5.3 EM Algorithm; 6.5.4 Gaussian Mixture Example; 6.6 The Bottom Line; 6.7 Problems; 7: Many Mini Topics; 7.1 Introduction; 7.2 k-Nearest Neighbors; 7.3 Neural Networks; 7.4 Boosting; 7.4.1 Football Analogy; 7.4.2 AdaBoost; 7.5 Random Forest; 7.6 Linear Discriminant Analysis; 7.7 Vector Quantization; 7.8 Naïve Bayes; 7.9 Regression Analysis. 7.10 Conditional Random Fields7.10.1 Linear Chain CRF; 7.10.2 Generative vs Discriminative Models; 7.10.3 The Bottom Line on CRFs; 7.11 Problems; 8: Data Analysis; 8.1 Introduction; 8.2 Experimental Design; 8.3 Accuracy; 8.4 ROC Curves; 8.5 Imbalance Problem; 8.6 PR Curves; 8.7 The Bottom Line; 8.8 Problems; II: Applications; 9: HMM Applications; 9.1 Introduction; 9.2 English Text Analysis; 9.3 Detecting Undetectable Malware; 9.3.1 Background; 9.3.2 Signature-Proof Metamorphic Generator; 9.3.3 Results; 9.4 Classic Cryptanalysis; 9.4.1 Jakobsen's Algorithm; 9.4.2 HMM with Random Restarts. |

Responsibility: | Mark Stamp. |

### Abstract:

"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 AuthorMark 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."--Provided by publisher.

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