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  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 explains 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.
 Special Interest Groups within the ML group focus on such domains as Probabilistic Logic Learning, Action and Activity Learning, Inductive Databases, Bio-informatics applications, Graphs and Supervised Learning. Cross-pollinations between Special Interest Groups occur through joint interdisciplinary research projects.
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