Machine learning (ML) is tightly coupled with big data. Not only is ML integral for making sense of large volumes of data, but it also forms an important theoretical foundation of data analytics. ML underlies many techniques for big data handling, knowledge extraction, and sense-making and is important for:
- Using dynamic data for decision making
- Handling unstructured and dynamic data, and developing computational approaches to working with large amounts of data using deep learning
- Using data mining to extract patterns and trends, and detect anomalies, including for massive scale and real-world datasets
- Developing interactive ML systems, essential as sensing and computing technologies become commonplace in our lives
ML research is important for progress in several IDEaS priority application areas, including healthcare, education, logistics and operations, physical data or sensors, social computing, and information systems, security, and privacy.
Irfan Essa, Director
Machine learning involves constructing algorithms that can analyze and learn from data in order to categorize such data and make related predictions. It includes both work at the foundational level, such as computational statistics, mathematical optimization, high-dimensional probability, decision theory, and work at the application level. The Center for Machine Learning is a new, interdisciplinary research center that brings together faculty and students from across Georgia Tech, provides a forum for them to interact and improve upon each others’ work, and presents a unified point of engagement with the Institute on collaborative projects.