This course offers students an introduction to the fundamentals conceptual, operational and experiential aspects of machine learning, or broadly defined algorithmic capability to manipulate, process, amend, and analyze data using appropriate applications. This is an introductory course, designed to endow students with the foundational theoretical and experiential knowledge of automated pattern detection approaches focused on four key outcomes of categorization, prediction, identification and detection, and further framed within the confines of supervised and unsupervised learning. The course is meant to offer an overview of this highly complex and rapidly evolving field; as such, it focuses on established approaches, key developmental trends, and hands-on applications of select techniques. Credits: 4
Prerequisites: DSE 6111