상세정보

Introduction
What is Machine Learning? ?
About This Book?
Necessary Background 
A Few Too Many Notes 

I TOOLS OF THE TRADE 
A Revealing Introduction to Hidden Markov Models 
Introduction and Background
A Simple Example 
Notation
The Three Problems 
The Three Solutions 
Dynamic Programming ?
Scaling?
All Together Now
The Bottom Line?

A Full Frontal View of Profile Hidden Markov Models?
Introduction 
Overview and Notation 
Pairwise Alignment 
Multiple Sequence Alignment 
PHMM from MSA 
Scoring 
The Bottom Line 

Principal Components of Principal Component Analysis 
Introduction?
Background 
Principal Component Analysis ?
SVD Basics ?
All Together Now 
A Numerical Example ?
The Bottom Line?

A Reassuring Introduction to Support Vector Machines 
Introduction?
Constrained Optimization 
AC loser Look at SVM 
All Together Now?
A Note on Quadratic Programming?
The Bottom Line?
Problems ?

A Comprehensible Collection of Clustering Concepts 
Introduction
Overview and Background
-Means 
Measuring Cluster Quality
EM Clustering 
The Bottom Line
Problems 

Many Mini Topics 
Introduction
-Nearest Neighbors 
Neural Networks 
Boosting
Random Forest 
Linear Discriminant Analysis 
VectorQuantization 
Naive Bayes
Regression Analysis 
Conditional Random Fields 

Data Analysis
Introduction 
Experimental Design 
Accuracy 
ROC Curves 
Imbalance Problem 
PR Curves 
The Bottom Line

II APPLICATIONS 
HMM Applications 
Introduction
English Text Analysis ?
Detecting "Undetectable" Malware?
Classic Cryptanalysis

PHMM Applications 
Introduction 
Masquerade Detection 
Malware Detection 

PCA Applications 
Introduction 
Eigenfaces 
Eigenviruses 
Eigenspam 

SVM Applications 
Introduction 
Malware Detection 
Image Spam Revisited 

Clustering Applications 
Introduction 
-Means for Malware Classification 
EM vs -Means for Malware Analysis 

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