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Machine learning represents a cutting-edge methodology gaining prominence across various scientific disciplines, including applied mathematics, engineering, computer science, and statistics.
These systems offer novel mathematical modeling frameworks rooted in differential equations. A key advantage lies in their ability to create adaptable applications for evolving environments, marking an exciting frontier where applied mathematics intersects with machine learning.
Our UCC mathematical modeling program focuses on equipping students with expertise in contemporary numerical methods and machine-learning tools. You'll tackle practical challenges by combining mathematical concepts with specialized software for complex networks and adaptive systems. Beyond modern applications, graduates acquire sought-after competencies in mathematical modeling, analytical thinking, computational science, adaptive machine learning, network analysis, and effectively communicating technical concepts.
The curriculum also emphasizes practical abilities like mathematical typesetting, technical writing, software development for desktop and web platforms, and proficiency in programming languages including C#, R, Python, and TensorFlow - all highly valued skills in today's job market.
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