I. Machine learning tools and techniques
1. What's it all about?
2. Input: Concepts, instances, and attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
- Inferring rudimentary rules
- Statistical modeling
- Divide-and-conquer: Constructing decision trees
- Covering algorithms: Constructing rules
- Mining association rules
- Linear models
- Instance-based learning
- Clustering
5. Credibility: Evaluating what's been learned
6. Implementations: Real machine learning schemes
7. Transformations: Engineering the input and output
8. Moving on: Extensions and applications
