Introduction to Machine Learning

Catalog description

Provides a broad introduction to machine learning and data mining. Includes topics such as supervised learning (feature selection, Bayesian classifier, nearest neighbors, logistic regression, decision trees, support vector machines) and unsupervised learning (dimensionality reduction, k-means clustering, hierarchical clustering, principal component analysis) methods. Prerequisite: CSE 1001 and ( MTH 2201 or MTH 3200 or MTH 3102 )