MSc in Mathematical Modelling & Machine Learning

at University College Cork

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

Modules
 

Students take 90 credits as follows:
 

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)

• AM6018 Dissertation in Mathematical Modelling and Self-Learning Systems (30 credits)
   

See the University Calendar (Mathematical Modelling*) for further course information. (*Note that the name for this programme is being amended from Mathematical Modelling & Self-Learning Systems to Mathematical Modelling & Machine Learning.)

• Candidates 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).

• Candidates are expected to have taken courses in mathematics, applied mathematics or statistics at 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.  For online modules, students are advised to have access to a laptop/home computer with an internet connection, modern browser, word processing and spreadsheet software.

• Candidates 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 candidates must ultimately be approved by the director of the MSc (Mathematical Modelling and Self-Learning Systems) programme.
   

  Minimum Requirements and Accepted English Language Tests & Examinations
  
  The minimum English language entry requirements for admission onto a postgraduate programme at UCC are outlined below. These requirements are for postgraduate applicants whose first language is not English. If Gaeilge (Irish) is your first language these requirements do not apply to you.
   

  There are a small number of programmes with a higher minimum English language entry requirement and we list these below under Programmes with a Higher Minimum English Language Requirement.
  
  IELTS*    

   

• 6.5 Overall

• 6.0 Individual Skills (academic version)

• TOEFL iBT    
   

• 90 Overall

• Listening 20

• Reading 19

• Speaking 21

• Writing 20

The EU fee for this course is €8,130.
 

The Non-EU fee for this course is €18,500.

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.