TU Graz keeps up with innovations in the digital sector, Source: TU Graz

New Machine Learning Centre in Graz connects university disciplines with big data

New Machine Learning Centre in Graz connects university disciplines with big data

Machine Learning could provide solutions to complex problems in finance, medicine, mobility and even climate models

Yesterday, the Technical University of Graz (TU Graz), Austria, announced that it will launch the Graz Centre for Machine Learning. The facility will help to develop Artificial Intelligence and process the enormous amounts of data generated through digitalisation.

Machine learning refers to a process where computers find automated solutions to complex problems using giant sets of data. This can be helpful in finance, mobility, medicine and even climate models.

Project Leader, Robert Legenstein, was quoted in a press release explaining that this technology has the potential to change the world similarly to the internet.

Drawing conclusions from big data

Graz University of Technology intends to significantly advance the further development of machine learning with its own research centre. Moreover, university authorities aim to integrate this research centre with other disciplines – making machine learning a more efficient and robust process.

All researchers who take part in the centre’s work will contribute data from their unique scientific fields and their regular research. The scientific work, on the other hand, will be divided into six core research areas, the basics of machine learning, as well as several flexible modules.

The core research areas are:

  • Visual Intelligence;
  • Optimization in Machine Learning;
  • Resource-efficient and brain-inspired Machine Learning;
  • Probabilistic Methods and Causal Models;
  • Recommender Systems and Behavioral Analytics;
  • Domain Specialized Machine Learning and Trust.

In addition to the core research areas, the application of machine learning methods is researched in a wide variety of domains, with experts from the field of machine learning working closely with representatives from other faculties.



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