CS 224 - Unsupervised Learning Models

2.0 Credits
Compare and apply unsupervised machine learning models using modeling andanalysis techniques such as clustering, dimensionality reduction, and association.This course is part of the Data Analytics for Professionals program.
Prerequisite Successful completion of CS 222or instructor permission.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:

Characterize when dimensionality reduction may be needed and use common techniques such as Principal Components Analysis, Latent Dirichlet Analysis and Singular Value Decomposition.
Apply key types of unsupervised learning and popular clustering methods such as k-means, k-modes, hierarchical clustering, and Gaussian Mixture Model.
Describe reinforcement learning and when it should be used.

Course Typically Offered
Winter