Machine Learning

Machine learning is the scientific discipline concerned with the design and development of algorithms and frameworks/theories that allow computers to learn from data to improve their performance on specific tasks. A machine learning algorithm learns from data to improve its performance on a specific task. The major focus of machine learning research is to automatically learn to recognize useful patterns and make intelligent decisions based on data.

In the School of Informatics and Computing, machine learning research spans from theoretical foundations to a broad range of applications including Web search, bioinformatics, chemical informatics, robotics, music recognition, sentiment analysis, computer vision, social link redirection, intelligent user interfaces, and the detection of abuse such as spam, fraud, astroturf, phishing, network anomalies, and so on.

Faculty in this area include:
Ariful Azad, David Crandall, Bradford Demarest, Eleftherios Garyfallidis, Roni Khardon, Minje Kim, David Leake, Lantao Liu, Filippo Menczer, Predrag Radivojac, Christopher Raphael, Allen Riddell, Luis Rocha, Michael Ryoo, Chung-chieh Shan, Dirk Van Gucht, Martha White, Donald Williamson, Grigory Yaroslavtsev, Qin Zhang, Yuan Zhou