Mathematical Modelling & Machine Learning

Course Outline

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.



Research Opportunities with Industrial Partners

Select students will have the opportunity to couple their research projects with industry-based internships, working within professional research and development teams with our industry partners, such as Cadence.

Modules (90 credits)

Part I (60 credits)

AM6004 Numerical Methods and Applications (5 credits)

AM6005 Nonlinear Dynamics (5 credits)

AM6007 Scientific Computing with Numerical Examples (10 credits)

AM6015 Computational Techniques with Networks (5 credits)

AM6016 Dynamic Machine Learning with Applications (5 credits)

AM6017 Complex and Neural Networks (5 credits)

AM6020 Open Source Infrastructure for Mathematical Modelling & Big Data (5 credits)

CS6421 Deep Learning (5 credits)

EE6024 Engineering machine Learning Solutions (5 credits)

ST4060 Statistical Methods for Machine Learning I (5 credits)

ST4061 Statistical Methods for Machine Learning II (5 credits)



Students who have taken any of the above modules in a previous degree must select alternative modules (subject to availability and timetabling) in consultation with the Programme Coordinator.



Part II (30 credits)

AM6021 Dissertation in Mathematical Modelling and Machine Learning (30 credits).

- Applicants must have obtained at least a Second Class Honours Grade II in a primary honours degree (NFQ, Level 8) or equivalent in a numerate discipline (i.e., commensurate with science or engineering programmes).

- Applicants are expected to have taken courses in mathematics, applied mathematics or statistics at the university level, and be familiar with calculus, vectors, matrices and elementary statistics. They are expected to have sufficient background in university-level mathematics as assessed by the course coordinator. In the case of competition for places selection will be made on the basis of primary degree results and/or interview.

- Applicants from Grandes Écoles Colleges are also eligible to apply if they are studying a cognate discipline in an ENSEA or EFREI Graduate School and are eligible to enter the final year (M2) of their programme.

- All applicants must ultimately be approved by the director of the MSc (Mathematical Modelling and Machine Learning) programme.



Note all students are advised to have access to a laptop/home computer with an internet connection, modern browser, word processing and spreadsheet software.



For Applicants with Qualifications Completed Outside of Ireland

Applicants must meet the required entry academic grade, equivalent to Irish requirements. For more information see our Qualification Comparison page.



International/Non-EU Applicants

For full details of the non-EU application procedure visit our how to apply pages for international students.

• In UCC, we use the term programme and course interchangeably to describe what a person has registered to study in UCC and its constituent colleges, schools, and departments.

• Note that not all courses are open to international/non-EU applicants, please check the fact file above. For more information contact the International Office.



English Language Requirements

Applicants who are non-native speakers of the English language must meet the university-approved English language requirements.

Closing Date:

Rolling deadline. Open until all places have been filled. Early application is advised.



Non-EU Closing Date

Open until all places have been filled or no later than 15 June. Early application is advised.

1 year full-time.

Start Date: 8 September 2025

Skills and Careers Information

Graduates with quantitative skills and expertise in mathematical modelling are in high demand in the industry according to the Government Expert Group on Future Skills Needs (EGFSN). Demand for these skills is projected to rise over the coming years not just in Ireland but in the EU and globally. We aim to provide our graduates with the competitive edge of knowledge into the mathematics that operate under the hood of modern modelling techniques.



Graduates from our MSc programme have secured jobs in the following areas: banking, financial trading, consultancy, online gambling firms, software development, logistics, data analysis, and with companies such as AIB, McAfee, Fexco, DeCare Systems, the Tyndall Institute, First Derivative, KPMG, TOMRA, Cadence and Qualcomm.