Artificial Intelligence & Machine Learning

At Georgia Tech, artificial intelligence (AI) and machine learning (ML) represent a large swath of faculty and research interests. We are concerned with constructing top-to-bottom and bottom-to-top models of human-level intelligence; building systems that can provide intelligent tutoring; creating adaptive and intelligent entertainment systems; making systems that understand their own behavior; growing our understanding of how to build autonomous agents that can adapt in dynamic environments involving multitudes of other intelligent agents, some of whom might be human; modeling and predicting human behavior; automating creativity; and addressing a variety of other problems. We advise Ph.D. and M.S. students in these areas, and we offer a broad set of undergraduate and graduate courses.

At the undergraduate level, AI and ML are mainly found in two threads: Intelligence and Devices. Commonly taken courses include Introduction to Artificial Intelligence, Machine Learning, Natural Language Understanding, Knowledge-based AI, Game AI and Pattern Recognition. Several courses in robotics and computational perception also have an AI or ML aspect. Versions of these courses are also available at the graduate level.

AI and machine learning often touch multiple areas and schools within the College of Computing, and different groups emphasize different aspects of the research. In the School of Interactive Computing, Ashok Goel, Charles Isbell, Ashwin Ram and Mark Riedl form a core of faculty, but many faculty in wearable computing as well as robotics and computational perception pursue related problems and apply similar techniques. There are also machine learning faculty in the schools of Computer Science and Computational Science & Engineering.

Coordinator: Charles Isbell