Course Descriptions and Schedule

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 data modeling for studies in which there is no clear distinction between explanatory and response variables. The objective may be to explain relationships among many continuous variables in terms of underlying dimensions, latent variables, or factors, as with principal components and factor analysis. The objective may be to find a lower-dimensional representation for multivariate cross-classified data, as with log-linear models. The objective may be to construct a visualization of variables or objects, as with traditional multidimensional scaling and t-distributed stochastic neighbor embedding. Or the objective may be to identify groups of variables and/or 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.

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

 



Spring 2021
Start/End DatesDay(s)TimeBuildingSection
03/29/21 - 06/06/21Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Mickelson, William
Online
Open

Spring 2021
Start/End DatesDay(s)TimeBuildingSection
03/29/21 - 06/06/21Optional Sync Tu
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Chaturvedi, Anil
Online
Open

Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Mickelson, William
Online
Open

Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync M
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Chaturvedi, Anil
Online
Open
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