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1. Study
2. Postgraduate
3. Taught Courses
4. Masters
5. Mathematical Modelling and Machine Learning

Course Fact File

Code	MSCMML
Duration	1 Year
Teaching Mode	Full-time
Qualifications	MSc
NFQ Level	Level 9
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 30 June. Early application is advised.
Start Date	9 September 2024

Course Outline

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.

Programme Content

This is a full-time programme running for 12 months from the date of first registration for the programme.

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)

Course Practicalities

We place great emphasis on hands-on practical skills. There is a computer laboratory allocated solely for the use of MSc students. PCs are preloaded with all the required software and tools. Teaching hours, tutorials and practical demonstrations, usually take place in the morning.  The rest of the time, you are expected to do exercises, assignments and generally put in the time required to acquire key skills. Students are advised to have access to a laptop/home computer with an internet connection, a modern browser, word processing, and spreadsheet software.

Why Choose This Course

This MSc programme reflects a philosophy of cutting-edge teaching methods and pragmatism. We believe that our programme opens up multiple new possibilities for our graduates; it will provide you with a skill set that will make you stand out from the crowd in your original field of study. The final project is an excellent opportunity for you to showcase your abilities to future employers or to undertake a detailed study in a new area of interest. The course is extremely flexible in helping you realise your ambitions. 

Connected Curriculum

We encourage innovative teaching and learning practices at UCC and this is embodied in the delivery of this programme. Our accessible learning approach reflects our commitment to the Connected Curriculum where we emphasise the connection between students, learning, research, and leadership through our vision for a Connected University. Our staff from the School of Mathematical Sciences have made significant contributions to their discipline and will support you in making meaningful connections across the breadth of mathematics, statistics and computer technology.

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.

Cadence is a pivotal leader in electronic systems design, building upon more than 30 years of computational software expertise. The company applies its underlying Intelligent System Design strategy to deliver software, hardware, and IP that turn design concepts into reality.
 
Cadence customers are the world’s most innovative companies, delivering extraordinary electronic products from chips to boards to complete systems for the most dynamic market applications including hyperscale computing, 5G communications, automotive, mobile, aerospace, consumer, industrial, and healthcare. For eight years in a row, Fortune magazine has named Cadence one of the 100 Best Companies to Work For.
 
Cadence Ireland is in the process of establishing a global R&D Centre of Excellence in Cork. As part of this exciting growth phase, we are partnering with UCC around its machine learning project and have openings for internships and joint projects. Join us and make your mark! 

 

Over the past 50 years, TOMRA has transformed ideas and technology to create intelligent and pioneering tools that enable the circular economy with advanced collection and sorting systems, and food processing by employing sensor-based sorting and grading technology.

The Machine Intelligence Team in TOMRA Food is an R&D team with a mixture of Machine Learning and Software Engineers, based in Dublin. The team are focused on creating new solutions that leverage edge and cloud technologies to harness value in the data generated by our machines, as well as enabling new technologies and advanced AI techniques, to be applied directly to solve our customer's problems.

TOMRA Food sorters generate terabytes of data on a daily basis. It's our job to explore, process this data, and uncover the patterns which yield insights and ultimately value to our customers. Having the ability to explore, test, mathematically model and iterate through multiple ideas to ultimately answer a question are key attributes of the machine learning engineer.

The MSc in Mathematical Modelling & Machine Learning at UCC equips students with the fundamental skill set which will empower them in making the transition to industry-applied research and development. We look forward to partnering with UCC to help with developing and inspiring the next generation of industry-based researchers.

 

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, Matchbook.com, First Derivatives, and KPMG.

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.

Fees and Costs

How To Apply

The closing date for non-EU applications is Open until all places have been filled or no later than 30 June. Early application is advised.

Apply Now