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Mathematical Modelling & Self-Learning Systems

The primary aim of this course is to educate you in the theoretical and practical aspects of mathematical problem solving, mathematical model development with self-learning systems and creating software solutions and communication of results.

This course provides training in the use and development of reliable numerical methods and corresponding software. It aims to train graduates with a mathematical background to develop and apply their skills to the solution of real world problems. It covers the underlying mathematical ideas and techniques and the use and design of mathematical software and self-learning systems. Several application areas are examined in detail. It develops skills in mathematical problem-solving, scientific computing, application of dynamic machine learning and technical communication.

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Postgraduate
Taught courses
Masters
Mathematical Modelling and Self-Learning Systems

About This Course

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Fact File

Course Outline

MODULES

Course Practicalities

Why choose this course?

Skills and Careers Information

Fact File

Title

MSc Mathematical Modelling and Self-Learning Systems

Code

CKR56

College

Science, Engineering and Food Science

Duration

1 Year

Teaching Mode

Full-time

Qualifications

MSc

EU Fees 2018

€7,000
See Fees and Costs for full details.

Entry Requirements

See Requirements for full details.

Closing Date

Applications processed in rounds: See How To Apply section for full details

Start Date

10th September 2018

Course Outline

The primary aim of this course is to educate you in the theoretical and practical aspects of mathematical problem solving, mathematical model development with self-learning systems and creating software solutions and communication of results.

This course provides training in the use and development of reliable numerical methods and corresponding software. It aims to train graduates with a mathematical background to develop and apply their skills to the solution of real world problems. It covers the underlying mathematical ideas and techniques and the use and design of mathematical software and self-learning systems. Several application areas are examined in detail. It develops skills in mathematical problem-solving, scientific computing, application of dynamic machine learning and technical communication.

Training is also provided in general computing skills, mathematical typesetting, mathematical writing, desktop and web-based mathematical software development, and the use of computer languages and packages including C#, R, Python and TensorFlow.

Course Practicalities

The course places 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. For online modules, students are advised to have access to a laptop/home computer with internet connection, modern browser, word processing and spreadsheet software.

Why choose this course?

This MSc reflects a philosophy of cutting edge teaching methods and pragmatism. As well as providing you with a host of abilities which are in demand in industry, this MSc provides skills which are complementary to most scientific and engineering undergraduate courses. The MSc not only opens up new possibilities, you also gain a set of skills that sets you apart 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.

Entry requirements

Requirements

Candidates must have obtained at least a 2.2 honours degree 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 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.

If you are applying with Qualifications obtained outside Ireland and you wish to verify if you meet the minimum academic and English language requirements for this programme please view the grades comparison table by country and for details of recognised English language tests.

Non-EU Candidates

Non-EU candidates are expected to have educational qualifications of a standard equivalent to Irish university primary degree level. In addition, where such candidates are non-native speakers of the English language they must satisfy the university of their competency in the English language. To verify if you meet the minimum academic requirements for this programme please visit our qualification comparison pages.

For more detailed entry requirement information please refer to the International website .

Duration

1 year full-time

Careers or further progression

Skills and Careers Information

Graduates with quantitative skills and expertise in self-learning algorithms are in high demand in industry according to the Governments Expert Group on Future Skills Needs. 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.

Subjects taught

Part 1
AM6004 Numerical Methods and Applications (5 credits)
AM6005 Non-linear Dynamics (5 credits)
AM6007 Scientific Computing (10 credits)
AM6013 Statistical, Dynamical, and Computational Modelling (10 credits)
AM6015 Computational Techniques with Networks (5 credits)
AM6016 Dynamic Machine Learning with Applications (5 credits)
AM6017 Complex and Neural Networks (5 credits)
ST4400 Data Analysis II (5 Credits)
ST4060 Computer Intensive Statistical Analytics I (5 Credits)
ST4061 Computer Intensive Statistical Analytics II (5 Credits)

Part 2
AM6012 Dissertation in Mathematical Modelling and Self- Learning Systems (30 credits)

Enrolment and start dates

Start Date: 10th September 2018

Remember to mention gradireland when contacting institutions!