Machine Learning

• Data, feature extraction and PCA
• Measures of similarity, summary statistics and probabilities
• Probability densities and data visualization
• Decision trees and linear regression
• Overfitting, cross-validation and Nearest Neighbor
• Performance evaluation, Bayes, and Naive Bayes
• Artificial Neural Networks and Bias/Variance
• AUC and ensemble methods
• K-means and hierarchical clustering
• Mixture models and density estimation
• Association mining