I would cover some fairly basic topics: 1. Naive Bayes (with review of probability needed to understand) 2. Linear Regression 3. Decision Trees (for a nonlin

I would cover some fairly basic topics: 1. Naive Bayes (with review of probability needed to understand) 2. Linear Regression 3. Decision Trees (for a nonlinear model) 4. Boosting (and why it doesn't overfit much) 5. k-means (and other clustering) Be sure to emphasize cross validation or a holdout set to evaluate all these models. The new edition of the "WEKA" book would be a nice text, rather than the original papers. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition Ian H. Witten, Eibe Frank, Mark A. Hall