Machine Learning is a discipline in itself. It is usually connected to specific application domains (like Computer Vision, Computational Finance, Natural Language Processing and Robotics) due to the fact that its realisation requires the use of data, thus, leading to Data Driven Analytics. Having the above in mind, in the world of digitalisation, Machine Learning resembles Mathematics in Natural Sciences. Our work focuses on new techniques and methodologies for teaching machines on how to learn from data.
Our research in Machine Learning and Computational Intelligence models allows us to target applications involving many types of data, like images, time-series, sensor data, generic graph structures, attribute data and multi-faceted data. Our current contributions are in the fields of Computer Vision, Computational Finance, Bioscience, Robotics and Social/Financial Networks. Since Machine Learning is expanding in many more application domains, it is expected that our work will span an increasingly high number of applications in the near future.