COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Clustering Techniques for Unsupervised Machine Learning – Radford
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Benjamin J. Radford, PhD
Principal Data Scientist
Sotera Defense Solutions
Clustering Techniques for Unsupervised Machine Learning
Monday, November 13, 4:30-5:45
Exploratory Hall, Room 3301
Abstract: Cluster analysis represents a broad class of unsupervised algorithms that are applicable to a variety of data science problems. An overview of some clustering models is provided and example use cases for clustering are discussed. Multivariate Gaussian mixture models are then discussed in detail and estimation techniques are outlined. K-selection is also discussed in the context of Gaussian mixture models. The talk concludes with a short discussion about how clustering techniques might be used in the context of cybersecurity.
Dr. Radford is a Principal Data Scientist at Sotera Defense Solutions where he works on data-driven cybersecurity research programs for the Department of Defense. He received his Ph.D. in political science from Duke University in 2016. His research interests include political methodology, security and political conflict, the political implications of cyberspace, and automated event data coding. Dr. Radford’s dissertation demonstrated the semi-automated population of dictionaries for event-coding in novel domains. He is also an avid guitarist.