The "machine learning" research group is a part of the Faculty of Engineering and Information Technologies at the International Burch University. It is led by Prof. Dr. Abdulhamit Subasi and counts 8 Ph.D. students representing virtually all areas of machine learning and data mining. The group focuses on machine learning and data mining research involving structured data, symbolic, logical and probabilistic representations, and background knowledge and applies it techniques to challenging domains in the life sciences and action- and activity learning.
Machine learning research at the International Burch University was initiated in 2009 when Abdulhamit Subasi comes to University and start to work as Dean of Faculty. In 2010, the group rapidly gained recognition for its seminal contributions to inductive logic programming. During this period, members of the group did a number of well-known scientific researches, many of which are now gathered in our wide-spread tool, and the activities of the group rapidly expanded into domains such as reinforcement learning and distance-based learning. Since then, the group focuses on applications in the life sciences (especially chemo- and bioinformatics), in constraint-based data mining and inductive databases, and statistical relational learning (combining probabilistic models with logic). It is now one of the largest machine learning labs groups in this part of Europe.
Machine learning is the subfield of artificial intelligence and computer science that studies how machines can learn. A machine learns when it improves its performance on specific tasks with experience. In order to learn, machine learning methods analyze their past experience in order to find useful regularities, which explain why machine learning is closely related to data mining. The machine learning group is investigating all types of machine learning and data mining problems and techniques, though it focuses on dealing with structured data (such as graphs, trees, and sequences), symbolic, logical and relational representations, and the use of knowledge and constraints. The group is well-known for its work on inductive logic programming, (statistical) relational learning, relational reinforcement learning, decision tree learning, graph mining, and inductive databases and constraint-based mining. It also studies applications in the life sciences and action- and activity learning.