Unsupervised Learning Methods

1 Unit · Graduate

This course introduces data modeling for studies in which there is no clear distinction between explanatory and response variables. Students learn how to explain relationships among many continuous variables in terms of underlying dimensions, as with principal components and factor analysis. They find lower-dimensional representations for multivariate cross-classified data, as with log-linear models. They visualize data with traditional multidimensional scaling and t-distributed stochastic neighbor embedding. And they identify groups of variables and objects that are similar to one another, as with cluster analysis and biclustering. Students work on research and programming assignments, exploring multivariate data and methods. (Required: MSDS 400-DL and MSDS 401-DL .)