Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
MSDS 411-DL : Unsupervised Learning Methods
Description
This course introduces traditional and modern methods of
unsupervised learning. Students see how to represent relationships
among many continuous variables using principal components and
factor analysis. They identify groups of individuals and groups of
variables with cluster analysis and block clustering. They explore
relationships among categorical variables with log-linear models
and association rules. They visualize multivariate data with
lattice displays, multidimensional scaling, and t-distributed
stochastic neighbor embedding. And they detect anomalies using
autoencoders and probabilistic deep learning. This is a
project-based course with extensive programming assignments.
This is a required course for the Analytics and Modeling
specialization.
Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS
401-DL Applied Statistics with R
