The introductory curriculum provides the mathematical and computer science foundations for supervised machine learning. This covers an introduction to Python, basic linear algebra, the ML pipeline, regression and SVM, and application to real-life datasets. Upon conclusion of the program, students should be able to carry out a full analysis with machine learning on real-life datasets to build predictive models. This includes pre- and post-processing, training, testing, and validation. The program is two days over a weekend, 8 hours per day.