Machine Learning Group

Research

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 focus on domains such as

• Probabilistic Logic Learning,
• Action and Activity Learning,
• Inductive Databases,
• Bio-informatics applications,
• Graphs and
• Supervised Learning

Projects

Education

The professors of the Machine Learning group are responsible for courses in the domain of machine learning, artifical intelligence, Prolog, ....

People

• 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!

Publications