Practical Machine Learning Methods

Catalog description

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.