The core aim of the MRes in AI and ML is to train students in formulating, conducting and assessing research in the AI and machine learning field, and provide focussed underpinning knowledge of AI. The learning and teaching approaches reflect these aims. The main three components of the programme are:
- the ‘AI foundation’ courses (Python and ‘Ethics, Privacy, AI in Society’ training)
- Research-skills courses, including literature review and simulated R&D project proposal
- the large independent research project including thesis
| Module name | ECTS | Term |
|---|---|---|
| MRes Individual research project - AI and Machine Learning | 60 | all year |
| Simulated Research & Development and Project Proposal | 10 |
Autumn and spring terms |
| Research Tutorials in AI and Machine Learning | 10 | Autumn and spring terms |
| Python Programming | 5 | Autumn term |
| Ethics, Privacy, AI in Society | 5 | Spring term |
| All modules are core and compulsory. Modules shown are for the current academic year and may be subject to change depending on special circumstances. | ||
The modules Python Programming and Ethics, Privacy, AI in Society aim to provide students with training in form of AI/ML and application courses to strengthen their skills in programming and AI technologies. You will also be able to master programming for advanced AI (e.g. Python) and machine learning; will have been taught modules in law and ethics, and a two-term series of tutorials (reading group model) will develop insight into a wide range of state-of-the-art AI algorithms and applications.
To develop in-depth study and research skills, students will undertake a literature review and simulated R&D proposal exercise in fields related to their main project area. This will help you develop your ability to independently shape and evidence a rigorous research and development plan. You will also learn how to present a technology business case (or grant proposal) addressed to different stakeholders and audiences.
The major independent research project will allow for the full exploration of AI and ML approaches in an area that you study in depth. Supervision and assessment will be based on progress milestones, including a poster presentation, an MRes thesis and an oral exam, all contributing to the final assessment.
The variety of teaching & learning methods used throughout the programme helps ensure that the students who have different learning styles - and who may come to the MRes from a diverse set of backgrounds in study or employment - benefit from learning in an environment where they feel included and where they are taught in ways that support their needs as individual
The Programme specifications can be found here.