Practical Machine Learning Methods

1 Course Unit

This course prepares students with no background in machine learning or data science to use tools from those fields effectively in applied contexts. Using GUI-based software - or optionally, by programming with libraries - students will build skills including (1) feature representations of spreadsheet-based or text datasets; (2) training classification and regression models for prediction tasks; (3) evaluation of machine learning model accuracy and error analysis; and (4) reasoning about predictive models and making tradeoffs like bias vs. variance, granularity and annotation complexity in labeled training data, and the ethical application of predictive modeling to human-centered data. Spring 1 Course Unit