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From self-driving cars to chess playing computers, from autonomous booking agents to automated financial trading systems, applications of Artificial Intelligence (AI) are having massive impacts in ever growing aspects of our lives. The main driver of this growth has been the Machine Learning (ML) technologies that underpin these high-profile AI successes. For the first time, the availability of large data sets needed to train sophisticated ML solutions is matched by the computational power to learn from this data. The result has been an explosion in the successful application of ML technologies to real-world problems in a variety of industries, from advertising and commerce to agriculture and healthcare, and from transport to manufacturing.

The Centre for Research Training in Machine Learning is designed to address the urgent industry demand for ML talent. The centre will produce academically outstanding, industry-ready PhD graduates in tightly connected cohorts. These graduates will be future leaders managing the disruption that ML is causing across industry and society, and will strengthen the reputation of Ireland as a global hub for ML education, research, and application.

The centre is a collaboration between University College Dublin (UCD), Dublin City University (DCU), and the Technological University of Dublin (TUD). It brings together 57 ML-focused, internationally recognised supervisors who work at the cutting-edge of ML research and its application. Students will benefit from a world-class, inter-institutional programme in a mature interdisciplinary environment that emphasises state-of-the-art research with an industry-relevant and entrepreneurial focus. The activities at the centre are built around four pillars:

  • ML Fundamentals: The fundamental theory, algorithms, techniques, and technologies on which ML is based.
  • ML in Society: From the displacement of jobs to the creation of filter bubbles, ML is having an enormously transformative effect on society which needs to be examined, understood, addressed, and communicated.
  • ML Practice: As ML technologies have moved out of the lab, a body of best practice has emerged around how to design, develop, deploy, and maintain ML solutions; as well as how to organise the teams that do this work and the projects that they do.
  • ML Applications: ML is having a disruptive effect on industries from fashion to agriculture which is driving new ways of operating in these industries and new ML approaches to match industry-specific demands.