Unsupervised Learning in Autonomous Systems

Catalog description

Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Using probabilistic programming languages, students will learn how to develop, interpret and deploy unsupervised machine learning models within autonomous systems. Common machine learning algorithms for unsupervised learning will be leveraged: k-means clustering, principal component analysis, non-negative matrix factorization, singular decomposition, and density-based spatial clustering of application with noise.