Introduction to Machine Learning

Catalog description

This course will provide an overview of the practice of machine learning. Supervised learning topics will include linear and generalized linear models, tree-based methods, kernel methods, and neural networks for regression and classification. Supervised learning model evaluation and the use of Python to clean data, train models, and create visuals. Unsupervised learning topics will include clustering and dimensionality reduction. Special attention will be paid to foundational concepts of machine learning: bias variance tradeoff, the curse of dimensionality, and model interpretability.