Machine Learning

UDRI researcher at work in UD's Mumma Radar Lab

Systems that Adapt and Adjust

According to American computing pioneer Arthur Samuel, machine learning is the ability of computers "to learn without being explicitly programmed." Machine learning is useful in computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include e-mail filtering, network intrusion or data breach detection, optical character recognition (OCR), and computer vision.

UDRI’s researchers are working to develop and implement flexible systems that automatically adjust to the needs of users according to the input they provide. Our systems see connections between data sources and user inputs, and sorts these pieces based on various metadata. This allows system users to sift through data based on how they want to look at the data rather than how the system sorts or stores data.

Metadata includes information such as file type, creation date, source, and so on, automatically added through a deep learning engine; system users can enter custom metadata tags and information to data sources. UDRI-developed deep learning algorithms then improve user awareness of available data, suggest areas for further investigation, and make connections between users and information to ensure analyses have more breadth and depth than a single user working alone. Our systems learn to understand how data products are developed, foster outside-the-box learning for users, and improve workflows.

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