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EE52037: Research project

[Page last updated: 16 August 2024]

Academic Year: 2024/25
Owning Department/School: Department of Electronic & Electrical Engineering
Credits: 30 [equivalent to 60 CATS credits]
Notional Study Hours: 600
Level: Masters UG & PG (FHEQ level 7)
Period:
Dissertation Period
Assessment Summary: CWDI 80%, CWOI 20%
Assessment Detail:
  • Research project presentation (CWOI 20%)
  • Research project (CWDI 80%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Learning Outcomes: Consider and take appropriate action for economic, ethical and sustainable issues which may impact on your project outcomes.
Develop evidence-based conclusions and recommendations.
Effectively communicate the outcomes to a range of audiences.
Implement and critically evaluate novel research in the context of existing literature.
Select and critically evaluate technical literature and other sources of information to solve a complex research problem.
Select appropriate methodology, justifying and evaluating chosen methods.


Synopsis: Identify, explore, and interpret aspects at the forefront of AI/ML applications through a research project. With guidance from an academic supervisor, you'll design and manage a project focused on an area of your choice. You'll use skills and knowledge developed so far on the course to disseminate your research outcomes to a range of audiences.

Aims: Your research project will act as platform for you to identify, explore and interpret aspects at the forefront of AI / ML applications and implement a programme of research. Under the guidance of an academic supervisor, you will design and manage your own project using skills and knowledge developed during the course and disseminate your research outcomes to a range of audiences.

Skills: Plan, implement and communicate an independent research project.

Content: Under the guidance of an academic supervisor, you will undertake a research project focused on an issue of your choice.
You will define and investigate the issue to propose evidence-based conclusions and recommendations. External organisations may also be involved with research projects.
Choices will be informed by your personal interests, the fulfilment of the aims of the unit, the availability of expert supervision, and the accessibility of relevant material.

Course availability:

EE52037 is a Must Pass Unit on the following courses:

Department of Electronic & Electrical Engineering
  • TEEE-AFM22 : MSc Artificial Intelligence for Engineering and Design
  • TEEE-AWM22 : MSc Artificial Intelligence for Engineering and Design

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.