### Overview

**NFQ Level 9, Major Award**

The MSc in Mathematical Modelling and Machine Learning is a full-time, blended learning programme running for 12 months from the date of first registration for the programme.

### Postgraduate Diploma in Mathematical Modelling and Machine Learning

Regulations regarding progression from Part I to Part II can be found in Marks and Standards. Students who pass Part I but do not meet these, or who choose not to progress to Part II and exit the programme will be conferred with the Postgraduate Diploma in Mathematical Modelling and Machine Learning.

### Programme Requirements

For information about modules, module choice, options and credit weightings, please go to Programme Requirements.

### Programme Requirements

Module List Code | Title | Credits |

| |

| |

AM6004 | Numerical Methods and Applications | 5 |

AM6005 | Nonlinear Dynamics | 5 |

AM6007 | Scientific Computing with Numerical Examples | 10 |

AM6015 | Computational Techniques with Networks | 5 |

AM6016 | Dynamic Machine Learning with Applications | 5 |

AM6017 | Complex and Neural Networks | 5 |

AM6020 | Open Source Infrastructure for Modelling and Big Data | 5 |

CS6421 | Deep Learning | 5 |

EE6024 | Engineering Machine Learning Solutions | 5 |

ST4060 | Statistical Methods for Machine Learning I | 5 |

ST4061 | Statistical Methods for Machine Learning II | 5 |

| |

AM6021 | Dissertation in Mathematical Modelling and Machine Learning | 30 |

Total Credits | 90 |

### Examinations

Full details and regulations governing Examinations for each programme will be contained in the *Marks and Standards Book* and for each module in the *Book of Modules*.

### Programme Learning Outcomes

**Programme Learning Outcomes for MSc (Mathematical Modelling and Machine Learning) (NFQ Level 9, Major Award)**

On successful completion of this programme, students should be able to:

- Apply the basic concepts, theories, principles and practical methods of mathematical modelling and machine learning to analyse and solve theoretical and practical problems;
- Give clear and organized written and verbal explanations of ideas in the areas of mathematical modelling and machine learning;
- Critically discuss and evaluate the concepts and examples in several areas of mathematical modelling and machine learning;
- Extend the given course material to solve original problems and develop associated computer codes;
- Contribute effectively as members of project teams dealing with mathematical models and their computer implementation, including the delivery of oral presentations and written reports.