People
 The ML group counts about 30 researchers.
 Special interest groups cooperate 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!
Projects
 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

Publications
Here are some key publications of the ML group:
 Guns, Tias, Nijssen, Siegfried, De Raedt, Luc. Itemset mining: a constraint programming perspective, Artificial Intelligence, 2011.
 Guy Van den Broeck, On the completeness of firstorder knowledge compilation for lifted probabilistic inference. NIPS 2011.
 Bernd Gutmann, Ingo Thon, Luc De Raedt: Learning the Parameters of Probabilistic Logic Programs from Interpretations. ECML/PKDD (1) 2011: 581596
 De Raedt, Luc; Kersting, Kristian; Kimmig, Angelika; Revoredo, Kate; Toivonen, Hannu, Compressing probabilistic Prolog programs, Machine learning, volume 70, issue 23, pages 151168, 2008
 Kersting, Kristian; De Raedt, Luc, Bayesian Logic Programming: Theory and Tool, an Introduction to Statistical Relational Learning, pages 291322, 2007
 Blockeel, Hendrik; Dehaspe, Luc; Demoen, Bart; Janssens, Gerda; Ramon, Jan; Vandecasteele, Henk, Improving the efficiency of inductive logic programming through the use of query packs, Journal of artificial intelligence research, volume 16, pages 135166, 2002
 Ramon, Jan; Bruynooghe, Maurice, A polynomial time computable metric between point sets, Acta informatica, volume 37, issue 10, pages 765780, 2001
 Dzeroski, S; De Raedt, Luc; Driessens, Kurt, Relational reinforcement learning, Machine learning, volume 43, issue 12, pages 752, 2001
 Kosala, Raymondus; Blockeel, Hendrik, Web mining research : A survey, SIGKDD Explorations  Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data M, volume 2, issue 1, pages 115, 2000
 Dehaspe, Luc; Toivonen, H, Discovery of frequent DATALOG patterns, Data mining and knowledge discovery, volume 3, issue 1, pages 736, 1999
 Blockeel, Hendrik; De Raedt, Luc, Topdown induction of firstorder logical decision trees, Artificial intelligence, volume 101, issue 12, pages 285297, 1998
 Blockeel, Hendrik; De Raedt, Luc; Ramon, Jan, Topdown induction of clustering trees, Proceedings of the 15th International Conference on Machine Learning, pages 5563, 1998
 Muggleton, Stephen; De Raedt, Luc, Inductive logic programming: theory and methods, Journal of logic programming, volume 19+20, pages 629679, 1994
