STAT 437

Credit: 3 OR 4 hours.

Unsupervised learning is a type of machine learning that deals with finding patterns in data without the use of labeled examples. Two major unsupervised learning techniques, clustering and dimensionality reduction, will be covered with a focus on methods, evaluation metrics, and interpretation of results. The methodologies enable discovery of and inference about hidden insights contained in high-dimensional unlabeled data. Applications on real and artificial datasets are emphasized using programming languages such as Python.

3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410 and either MATH 415 or MATH 257.