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Mathematical modelling creates abstract descriptions of real-world systems that can be used to gain insights into relationships and make predictions. Machine learning techniques are innovative approaches to mathematical modelling that are data-driven and use differential or difference equations at their foundation.

As the modern world becomes more reliant on AI technology, we face the emerging and inevitable need for more efficient use of data and the explainability of the machine learning methods being used in increasingly sensitive application areas, such as finance and healthcare. Our graduates do not only learn how to use machine learning methods, but also learn the mathematics, including dynamical systems theory and network science, that underpin how modern machine learning functions.

The new frontier of machine learning involves the development and application of smarter techniques that either exploit an understanding of how machine learning systems learn from data when creating a model or exploit a mechanistic understanding of the system being modelled. This is an exciting and rapidly developing area at the interface between applied mathematics and machine learning.

The primary aim of our mathematical modelling programme at UCC is to provide you with training in the use and development of modern numerical methods and machine-learning software. You will develop and apply new skills to real-world problems using mathematical ideas and techniques together with software tailored for complex networks and self-learning systems. While there is a strong focus on modern applications, our graduates will gain in-demand skills in problem-solving, presentation and communication of mathematical ideas, and scientific computing using computer languages and packages such as Python, R, C# and TensorFlow – all of which are highly prized by employers in this field.

Generally, our MSc students have completed undergraduate degrees in numerate disciplines such as engineering, computer science, physics, or other areas of mathematics. Our students attain the practical skills that are in demand by the industrial sector, as well as the theoretical foundation required for research into deeper understandings of modern modelling methods and the development of innovative approaches to using these methods. The course is designed to allow graduates to stand out to employers and take up leading roles on route to more efficient and explainable modelling and machine learning practices.