Machine Learning

The broad goal of machine learning is to automate the decision-making process, so that computer-automated predictions can make a task more efficient, accurate, or cost-effective than it would be using only human decision making. Carnegie Mellon is widely regarded as one of the world’s leading centers for machine learning research, and the scope of our machine learning research is broad. Our current research addresses learning in games, where there are multiple learners with different interests; semi-supervised learning; astrostatistics; intrusion detection; and structured prediction.

Our is distinguished by its serious focus on applications and real systems. A notable example from machine learning is research that has led a system for early detection of disease outbreaks. Carnegie Mellon has also received ongoing recognition from its Robotic soccer research program, which provides a rich environment for machine learning that “improves with experience,” involving problem solving in complex domains with multiple agents, dynamic environments, the need for learning from feed-back, real-time planning, and many other artificial intelligence issues.

Faculty working in this area:

Lastsort descending First Title Email
Blum Avrim Faculty
Carbonell Jaime Director LTI; Newell University Professor
Fahlman Scott Emeritus Faculty
Faloutsos Christos Professor
Fink Eugene Senior Systems Scientist
Maxion Roy Research Professor
Mitchell Tom E. Fredkin University Professor; Faculty
Moore Andrew Professor and Dean
Sandholm Tuomas Professor
Shah Nihar Bhadresh Assistant Professor
Veloso Manuela M. Department Head for Machine Learning & Herbert A. Simon University Professor of Computer Science
Woodruff David Associate Professor
Xing Eric P. Professor
Subscribe to Machine Learning