1. Introduction to Collective Intelligence
- What is Collective Intelligence?
- What is Machine Learning
- Limits of Machine Learning
- Real-Life Examples
- Other Uses for Learning Algorithms
2. Making Recommendations
- Collaborative Filtering
- Collecting Preferences
- Finding Similar Users
- Recommending Items
- Matching Products
- Building a del.icio.us Link Recommender
- Item-Based Filtering
- Using the MovieLens Dataset
- User-Based or Item-Based Filtering?
3. Discovering Groups
4. Searching and Ranking
5. Optimization
6. Document Filtering
7. Modeling with Decision Trees
8. Building Price Models
- Building a Sample Dataset
- k-Nearest Neighbors
- Weighted Neighbors
- Cross-Validation
- Heterogeneous Variables
- Optimizing the Scale
- Uneven Distributions
- Using Real Data - the eBay API
- When to Use k-Nearest Neighbors
9. Advanced Classifications: Kernel Methods and SVMs
- Matchmaker Dataset
- Difficulties with the Data
- Basic Linear Classification
- Categorical Features
- Scaling the Data
- Understanding Kernel Methods
- Support-Vector Machines
- Using LIBSVM
- Matching on Facebook
10. Finding Independent Features
- A Corpus of News
- Previous Approaches
- Non-Negative Matrix Factorization
- Displaying the Results
- Using Stock Market Data
11. Evolving Intelligence
12. Algorithm Summary
- Bayesian Classifier
- Decision Tree Classifier
- Neural Networks
- Support-Vector Machines
- k-Nearest Neighbors
- Clustering
- Multidimensional Scaling
- Non-Negative Matrix Factorization
- Optimization
A. Third-Party Libraries
B. Mathematical Formulas
