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Machine Learning Group


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.



  • The ML group counts about 30 researchers.
  • Special interest groups co-operate and exchange knowledge in a wide range of research topics.
  • We are looking for PhD researchers on several research topics. Check out our job offers!


  • The ICON project aims to create a new ICT paradigm, called Inductive Constraint Programming, that bridges the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution of constraint satisfaction problems, but also to employ declarative constraint programming principles in data mining and machine learning.
  • GOA Probabilistic logic learning, sometimes also called statistical relational learning, is a newly emerging subfield of artificial intelligence lying at the intersection of knowledge representation, reasoning about uncertainty and machine learning. It aims at combining learning and probabilistic reasoning within first order logic representations.
  • More projects


Here are some key publications of the ML group: