Data Mining and Machine Learning- Simple examples: The weather and other problems- Fielded Applications- Machine Learning and Statistics- Generalization as Search- Data Mining and Ethics- Input: Concepts, Instances and Attributes- What’s a Concept?- What’s in an Attribute- Preparing the Input.

Tables- Linear Models- Trees- Rules- Instance-Based Representation- Clusters- 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.

Decision Trees- Classification Rules- Association Rules- Extending Linear Models- Instance-Based Learning- Numeric Prediction with Local Linear Models- Bayesian Networks- Clustering- Semi supervised Learning- Multi-Instance Learning.

Attribute Selection- Discretizing Numeric Attributes- Projections- Sampling- Cleansing- Transforming Multiple Classes to Binary Ones- Calibrating Class Probabilities- Ensemble Learning: Combining Multiple Models- Bagging- Randomization- Boosting- Additive Regression- Interpretable Ensembles- Stacking.

Applying Data Mining- Learning From Massive Datasets- Data Stream Learning- Incorporating Domain Knowledge- Text Mining- Web Mining- Adversarial Situations- Ubiquitous Data Mining- The Knowledge Flow Interface: Components- Configuring and Connecting the Components-Incremental Learning- Simple Data Mining Application