Mathematical Modelling & Machine Learning

University College Cork

Mathematical Modelling & Machine Learning

In particular, machine learning systems are innovative approaches to mathematical modelling which use differential equations at their foundation. A particular strength of this approach is that it ultimately allows you to design applications that can adapt to a changing environment. This is a new 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 mathematical modelling, problem-solving, scientific computing, dynamic machine learning, complex networks and communication of mathematical ideas to a non-technical audience.

We also teach general hands-on skills such as mathematical typesetting, mathematical writing, desktop and web-based mathematical software development, and the use of computer languages and packages such as C#, R, Python and TensorFlow – all of which are highly prized by employers in this field.

Subjects taught

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)

Entry requirements

- 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.

Application dates

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


1 year full-time.

Enrolment dates

Start Date: 9 September 2024

Post Course Info

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 and TOMRA Food.

Skills and Careers Information
Graduates with quantitative skills and expertise in self-learning algorithms 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. Graduates from a similar MSc 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, MpStor, the Tyndall Institute,, First Derivatives, and KPMG.

More details
  • Qualification letters


  • Qualifications

    Degree - Masters (Level 9 NFQ)

  • Attendance type

    Daytime,Full time

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    Course provider