Foundations for supervised learning (classification/regression): basic algorithms, overfitting, train and test sets, cross-validation, bias-variance tradeoff, regularization, ROC curve for binary classification (various R packages)
Slides here.
R code and data in the subdirs above.
Leo Breiman: Statistical Modeling: The Two Cultures
Also have a look at the best ML book ever: Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning, 2nd. ed., Springer, 2009