Machine Learning for Hackers

Preface; Machine Learning for Hackers; How This Book Is Organized; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgements; Chapter 1: Using R; 1.1 R for Machine Learning; Chapter 2: Data Exploration; 2.1 Exploration versus Confirmation; 2.2 What Is Data?; 2.3 Inferring the Types of Columns in Your Data; 2.4 Inferring Meaning; 2.5 Numeric Summaries; 2.6 Means, Medians, and Modes; 2.7 Quantiles; 2.8 Standard Deviations and Variances; 2.9 Exploratory Data Visualization; 2.10 Visualizing the Relationships Between Columns; Chapter 3: Classification: Spam Filtering; 3.1 This or That: Binary Classification; 3.2 Moving Gently into Conditional Probability; 3.3 Writing Our First Bayesian Spam Classifier; Chapter 4: Ranking: Priority Inbox; 4.1 How Do You Sort Something When You Don't Know the Order?; 4.2 Ordering Email Messages by Priority; 4.3 Writing a Priority Inbox; Chapter 5: Regression: Predicting Page Views; 5.1 Introducing Regression; 5.2 Predicting Web Traffic; 5.3 Defining Correlation; Chapter 6: Regularization: Text Regression; 6.1 Nonlinear Relationships Between Columns: Beyond Straight Lines; 6.2 Methods for Preventing Overfitting; 6.3 Text Regression; Chapter 7: Optimization: Breaking Codes; 7.1 Introduction to Optimization; 7.2 Ridge Regression; 7.3 Code Breaking as Optimization; Chapter 8: PCA: Building a Market Index; 8.1 Unsupervised Learning; Chapter 9: MDS: Visually Exploring US Senator Similarity; 9.1 Clustering Based on Similarity; 9.2 How Do US Senators Cluster?; Chapter 10: kNN: Recommendation Systems; 10.1 The k-Nearest Neighbors Algorithm; 10.2 R Package Installation Data; Chapter 11: Analyzing Social Graphs; 11.1 Social Network Analysis; 11.2 Hacking Twitter Social Graph Data; 11.3 Analyzing Twitter Networks; Chapter 12: Model Comparison; 12.1 SVMs: The Support Vector Machine; 12.2 Comparing Algorithms; Works Citedbooks and publicationsbibliography ofresourcesbooks and publications; website

Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.

John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.