Tutorial PowerPoint slides:

Presentation file I
• Tutorial overview and goals
• Importance of Machine Learning for discovery and decision support system construction
• A framework for inductive Machine Learning
• Generalization and overfitting
• Quick review of data preparation and model evaluation
• Bayesian classifiers

Presentation file II
• Neural Networks

Presentation file III
• Case Study: Predicting Breast Cancer Invasion with Artificial Neural Networks on the Basis of Mammographic Features

Presentation file IV
• Support Vector Machines

Presentation file V
• Quick Review of Additional families (K-Nearest Neighborhs, Clustering, Decision Tree Induction, Genetic Algorithms)

Presentation file VI
• Causal Discovery Methods Using Causal Probabilistic Networks

Presentation file VII
• Feature selection

Presentation file VIII
• Case studies

Presentation file IX
• Conclusions and wrap-up
• Resources for machine learning
• Questions & feedback