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DE Seminar: Francesca McFadden

Graduate Student Talk at UMBC


Mathematics/Psychology : 401

Date & Time

May 8, 2023, 11:00 am11:30 am


TitleUnsupervised Machine Learning Indicators of Data and Distribution Uncertainty 

Speaker: Francesca McFadden, advised by Dr. Matthias Gobbert

AbstractUtility of Machine learning techniques is predicated on the available training data which may be accessed, measured, or simulated. Machine learning techniques utilize the point estimates from a data set to surmise the data category, predict data estimates, and select features for algorithm evaluation. There are two major types of Machine learning algorithms - Supervised versus Unsupervised. Unsupervised techniques are applied to unlabeled data. Clustering is an unsupervised machine learning technique to group similar observations in the data set into a discrete number of clusters. The talk provides an overview of clustering algorithms and then highlights an application employing the Mahalanobis distance metric. The application is a methodology using unsupervised learning to expand current approaches to recognize when there is a lack of prediction competence for a supervised machine learning model. Model competence is an indicator of how well a model is expected to perform on inputs outside of its training set. Model competency metrics enable detection of when data being processed is significantly outside the prediction space of a machine learned model. Additional current and future applications of clustering algorithms being explored are discussed.