1. Chapter 1 Introduction to Collective Intelligence

   1. What Is Collective Intelligence?

   2. What Is Machine Learning?

   3. Limits of Machine Learning

   4. Real-Life Examples

   5. Other Uses for Learning Algorithms

2. Chapter 2 Making Recommendations

   1. Collaborative Filtering

   2. Collecting Preferences

   3. Finding Similar Users

   4. Recommending Items

   5. Matching Products

   6. Building a del.icio.us Link Recommender

   7. Item-Based Filtering

   8. Using the MovieLens Dataset

   9. User-Based or Item-Based Filtering?

   10. Exercises

3. Chapter 3 Discovering Groups

   1. Supervised versus Unsupervised Learning

   2. Word Vectors

   3. Hierarchical Clustering

   4. Drawing the Dendrogram

   5. Column Clustering

   6. K-Means Clustering

   7. Clusters of Preferences

   8. Viewing Data in Two Dimensions

   9. Other Things to Cluster

   10. Exercises

4. Chapter 4 Searching and Ranking

   1. What's in a Search Engine?

   2. A Simple Crawler

   3. Building the Index

   4. Querying

   5. Content-Based Ranking

   6. Using Inbound Links

   7. Learning from Clicks

   8. Exercises

5. Chapter 5 Optimization

   1. Group Travel

   2. Representing Solutions

   3. The Cost Function

   4. Random Searching

   5. Hill Climbing

   6. Simulated Annealing

   7. Genetic Algorithms

   8. Real Flight Searches

   9. Optimizing for Preferences

   10. Network Visualization

   11. Other Possibilities

   12. Exercises

6. Chapter 6 Document Filtering

   1. Filtering Spam

   2. Documents and Words

   3. Training the Classifier

   4. Calculating Probabilities

   5. A Naïve Classifier

   6. The Fisher Method

   7. Persisting the Trained Classifiers

   8. Filtering Blog Feeds

   9. Improving Feature Detection

   10. Using Akismet

   11. Alternative Methods

   12. Exercises

7. Chapter 7 Modeling with Decision Trees

   1. Predicting Signups

   2. Introducing Decision Trees

   3. Training the Tree

   4. Choosing the Best Split

   5. Recursive Tree Building

   6. Displaying the Tree

   7. Classifying New Observations

   8. Pruning the Tree

   9. Dealing with Missing Data

   10. Dealing with Numerical Outcomes

   11. Modeling Home Prices

   12. Modeling "Hotness"

   13. When to Use Decision Trees

   14. Exercises

8. Chapter 8 Building Price Models

   1. Building a Sample Dataset

   2. k-Nearest Neighbors

   3. Weighted Neighbors

   4. Cross-Validation

   5. Heterogeneous Variables

   6. Optimizing the Scale

   7. Uneven Distributions

   8. Using Real Data—the eBay API

   9. When to Use k-Nearest Neighbors

   10. Exercises

9. Chapter 9 Advanced Classification: Kernel Methods and SVMs

   1. Matchmaker Dataset

   2. Difficulties with the Data

   3. Basic Linear Classification

   4. Categorical Features

   5. Scaling the Data

   6. Understanding Kernel Methods

   7. Support-Vector Machines

   8. Using LIBSVM

   9. Matching on Facebook

   10. Exercises

10. Chapter 10 Finding Independent Features

    1. A Corpus of News

    2. Previous Approaches

    3. Non-Negative Matrix Factorization

    4. Displaying the Results

    5. Using Stock Market Data

    6. Exercises

11. Chapter 11 EVOLVING INTELLIGENCE

    1. What Is Genetic Programming?

    2. Programs As Trees

    3. Creating the Initial Population

    4. Testing a Solution

    5. Mutating Programs

    6. Crossover

    7. Building the Environment

    8. A Simple Game

    9. Further Possibilities

    10. Exercises

12. Chapter 12 Algorithm Summary

    1. Bayesian Classifier

    2. Decision Tree Classifier

    3. Neural Networks

    4. Support-Vector Machines

    5. k-Nearest Neighbors

    6. Clustering

    7. Multidimensional Scaling

    8. Non-Negative Matrix Factorization

    9. Optimization

1. Appendix Third-Party Libraries

   1. Universal Feed Parser

   2. Python Imaging Library

   3. Beautiful Soup

   4. pysqlite

   5. NumPy

   6. matplotlib

   7. pydelicious

2. Appendix Mathematical Formulas

   1. Euclidean Distance

   2. Pearson Correlation Coefficient

   3. Weighted Mean

   4. Tanimoto Coefficient

   5. Conditional Probability

   6. Gini Impurity

   7. Entropy

   8. Variance

   9. Gaussian Function

   10. Dot-Products

3. Colophon

The animals on the cover of Programming Collective Intelligence are King penguins (Aptenodytes patagonicus). Although named for the Patagonia region, King penguins no longer breed in South America; the last colony there was wiped out by 19th-century sealers. Today, these penguins are found on sub-Antarctic islands such as Prince Edward, Crozet, Macquarie, and Falkland Islands. They live on beaches and flat glacial lands near the sea. King penguins are extremely social birds; they breed in colonies of as many as 10,000 and raise their young in creches.

Standing 30 inches tall and weighing up to 30 pounds, the King is one of the largest types of penguin -- second only to its close relative the Emperor penguin. Apart from size, the major identifying feature of the King penguin is the bright orange patches on its head that extend down to its silvery breast plumage. These penguins have a sleek body frame and can run on land, instead of hopping like Emperor penguins. They are well adapted to the sea, eating a diet of fish and squid, and can dive down 700 feet, far deeper than most other penguins go. Because males and females are similar in size and appearance, they are distinguished by behavioral clues such as mating rituals.

King penguins do not build nests; instead, they tuck their single egg under their bellies and rest it on their feet. No other bird has a longer breeding cycle than these penguins, who breed twice every three years and fledge a single chick. The chicks are round, brown, and so fluffy that early explorers thought they were an entirely different species of penguin, calling them "woolly penguins." With a world population of two million breeding pairs, King penguins are not a threatened species, and the World Conservation Union has assigned them to the Least Concern category.

The cover image is from J. G. Wood's Animate Creation. The cover font is Adobe ITC Garamond. The text font is Linotype Birka; the heading font is Adobe Myriad Condensed; and the code font is LucasFont's TheSands Mono Condensed.