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Carnegie Mellon University, Machine Learning Department

The Machine Learning Department is an academic department within Carnegie Mellon University's School of Computer Science. We focus on research and education in all areas of statistical machine learning.

What is Machine Learning?

Machine Learning is a scientific field addressing the question "How can we program systems to automatically learn and to improve with experience?" We study learning from many kinds of experience, such as learning to predict which medical patients will respond to which treatments, by analyzing experience captured in databases of online medical records. We also study mobile robots that learn how to successfully navigate based on experience they gather from sensors as they roam their environment, and computer aids for scientific discovery that combine initial scientific hypotheses with new experimental data to automatically produce refined scientific hypotheses that better fit observed data.

To tackle these problems we develop algorithms that discover general conjectures and knowledge from specific data and experience, based on sound statistical and computational principles. We also develop theories of learning processes that characterize the fundamental nature of the computations and experience sufficient for successful learning in machines and in humans.

Please see our Technical Report on the Discipline of Machine Learning.

ML Research

ML has an energetic research effort focused on developing new statistical learning algorithms of general use, new foundational theories of learning and learnability, and new learning applications in areas ranging from robotics, to human brain imaging, to natural language text interpretation.

ML Education

ML launched the world's first Ph.D. program in "Computational and Statistical Learning" in 2002, (renamed as Machine Learning in 2006) and this program continues to grow each year. We offer a secondary Masters degree in this area, and several courses available to CMU's undergraduate students. Each summer the ML Faculty teach a three-day continuing education course on current algorithms and applications in statistical and computational learning.

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