Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning.
Research questions concern for instance:
- learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
- incorporation of invariances as well as expert domain knowledge in learning algorithms
- computational, sample, query, and communication complexity of learning algorithms
- constructive machine learning scenarios such as structured output prediction
- learning with small labelled data sets and large unlabelled data sets
- adverserial learning with mistake and/or regret bounds
- parallelisation/distribution of learning algorithms
- approximation of learning algorithms
- scalability of learning algorithms
- reliability of learning algorithms
- extreme learning
To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.
The research Unit Machine Learning is part of the Institute of Information Systems Engineering.
- Bachelor Thesis in Computer Science / 194.112 / PR
- Machine Learning / 184.702 / VU
- Machine Learning Algorithms and Applications / 194.101 / PR
- Machine Learning Theory Project / 194.109 / PR
- Scientific Research and Writing / 193.052 / SE
- Seminar for PhD Students / 194.110 / SE
- Seminar on Theoretical Aspects of Machine Learning Algorithms / 194.102 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
- Machine Learning for Chemical Synthesis / T. Gärtner, A. Haywood, J. Redshaw, A. Taylor, A. Mason, J. Hirst / The Royal Society of Chemistry, London, 2020, ISBN: 978-1-78801-789-3; 25 pages
Soon, this page will include additional information such as reference projects, conferences, events, and other research activities.
Until then, please visit Machine Learning’s research profile in TISS .