Master of Science in Mathematical Modelling and Machine Learning

Machine learning is an important and newly emerging technique in many areas of applied science such as applied mathematics, engineering, computer science and statistics.

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.

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.

Duolingo English Test: DET 120 (with a minimum score of 110 in each individual section)

IELTS: 6.5 Overall; 6.0 Individual Skills (academic version)

TOEFL iBT: 90 Overall; Listening 20; Reading 19; Speaking 21; Writing 20

Cambridge Examinations:

C2 Proficiency
C1 Advanced
B2 First

176 overall with no less than 169 in each element of the test

Pearson PTE: Minimum score of 63 with no section score below 59