Science is to test crazy ideas, Engineering is to put these ideas into Business: The Holzinger group is doing theoretical, algorithmical, and experimental studies to help to understand the problem of knowledge extraction from complex data to discover unknown unknowns. We try to help to answer a grand question: How can we perform a new task by exploiting knowledge extracted during problem solving of previous tasks. Contributions to this problem would have major impact to Artificial Intelligence generally, and Machine Learning specifically, as we could develop software which learns from previous experience – similarly as we humans do.
- Today the problem are heterogeneous, probabilistic, high-dimensional and complex data sets. The challenge of machine learning is to learn from such data, to extract knowledge, to make predictions, and to help to make decisions under uncertainty.
- The Holzinger Group works persistently on a synergistic combination of methods from two areas, offering ideal conditions to help to solve such problems: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with computational intelligence.
- This needs an international concerted effort without boundaries (see > Expert network), and a comittment to train the next generation of data scientists (interested students please proceed to the > MSc/PhD seminar). Serendipity is a desired effect of such cross-domain approaches, and shall cross-fertilize methodologies and theoretical insights, and ultimately transfer algorithmic developments into application domains for the benefit of the human (interested professionals > CD-MAKE).
Research Field: Data Science; Technical Area: Machine Learning; Application Area: Health Informatics