Lecture notes and supplemental material.
Basic working with data frames. Selection and counting without loops. Graphs, especially scatterplot and bar graph.
Graphs of results. Quality of fit (statistical tests). When do you have enough variables included?
Deciding from a discrete set of responses.
Cross-validation and resampling.
Selecting the best model. Avoiding overfitting.
The perceptron, maximal margin classifier, and possibly SVM.
A broad outline of what we plan to cover, so you can see how it all fits together.
Resources for machine learning that we refer to in the class.
The fundamental building blocks of linear algebra.