As the use of computers increase, so does the volume of data produced. The aim of this research area is designing computers that can learn from various data sources, including, documents, video, images, web sites, music, bioinformatics and time series data. Studies are conducted on training different types of machine learning models (Artificial Neural Networks, Support Vector Machines, Decision Trees etc.) by using data gathered from various sources. The group also works actively on social networks, feature selection, active learning, combining different types of classifiers, face recognition, video annotation, and machine learning theory. There is also a recent set of works on applying machine learning methods to help humans, especially people with disabilities, learn better.
Faculty Members Working in This Area: