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
• Bayesian Networks

Presentation file II
• Neural Networks

Presentation file III
• Support Vector Machines

Presentation file IV
• A Sampling of Various Other Learning Methods (Decision Trees, Genetic Algorithms, K-Nearest Neighbors, Clustering)

Presentation file V
• Discovery of Causal Structure Using Causal Probabilistic Network Induction

Presentation file VI
• Feature Selection
• Case Study: Categorizing Text into Content Categories
• Case Study: Diagnostic Model from Array Gene Expression Data
• Case Study: Imputation for Machine Learning Models for Lung Cancer Classification Using Array Comparative Genomic Hybridization

Presentation file VII
• Case Study: Causal Discovery Methods Using Causal Probabilistic Networks

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

Presentation file IX
• Conclusions and Wrap-up
• Resources for Machine Learning
• Questions & Feedback