Scientific Programming and Machine Learning

Catalog description

This course introduces students to the field of machine learning. Students learn the algorithms which underpin many popular machine learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. Topics include: supervised learning (regression [simple, multi, and logistic]), (classification [binary, multi, imbalanced]), and unsupervised learning (dimensionality reduction, principal components, cluster analysis).