What’s It All About?

What’s It All About?

1. 1.1 DATA MINING AND MACHINE LEARNING

   • Structural Patterns
   • Machine Learning

2. 1.2 SIMPLE EXAMPLES: THE WEATHER AND OTHER PROBLEMS

   • The Weather Problem
     • Outlook
     • Temperature
     • Humidity
     • Windy
   • CPU Performance: Introducing Numeric Prediction
   • Irises: A Classic Numeric Dataset
   • Labor Negotiations: A More Realistic Example

3. 1.3 FIELDED APPLICATIONS

   • Web Mining
   • Decisions Involving Judgment
   • Screening Images
   • Load Forecasting
   • Diagnosis
   • Marketing and Sales

4. 1.4 MACHINE LEARNING AND STATISTICS

5. 1.5 GENERALIZATION AS SEARCH

   • Enumerating the Concept Space
   • Bias

6. 1.6 DATA MINING AND ETHICS

   • Reidentification
   • Using Personal Information
   • Wider Issues