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

Last	First	Professional Title	Available To Advise?
Chen	Tianqi	Assistant Professor	✔
Fahlman	Scott	Research Faculty Emeritus	✖
Faloutsos	Christos	Professor	✔
Li	Minchen	Assistant Professor	✔
Mitchell	Tom	Founders University Professor, E. Fredkin University Professor	✔
Raghunathan	Aditi	Assistant Professor	✔
Sandholm	Tuomas	Angel Jordan University Professor of Computer Science	✔
Shah	Nihar	Associate Professor	✔
Woodruff	David	Professor	✔

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