Applications of AI in Electrical and Electronic Engineering
| Code | School | Level | Credits | Semesters |
| EEEE3129 | Electrical and Electronic Engineering | 3 | 10 | Autumn UK |
- Code
- EEEE3129
- School
- Electrical and Electronic Engineering
- Level
- 3
- Credits
- 10
- Semesters
- Autumn UK
Summary
This module provides an introduction to artificial intelligence (AI) for engineers who are curious about AI. The module considers what AI is, what it is used for and why it is used in engineering. The module avoids the mathematics of the subject and instead focusses on the concepts and application of AI in the context of AI as an engineering tool.
The module introduces the core concepts of AI (what is meant by AI, machine learning and deep learning), supervised vs unsupervised learning and examples of AI applied in engineering. This is then followed by a closer look at supervised learning and the data source/type requirements of AI. The core concepts will be reinforced through a number of case studies arising out of the field of electrical and electronic engineering for example intelligent manufacturing, autonomous robotics, computer vision and energy optimisation.
The module will consider the challenges associated with current AI and explore issues related to ethics and bias.
Target Students
Available to Students UG and PGT students within EEE. 3rd/4th year and MSc students in the Department of Electrical and Electronic Engineering.
Assessment
- 25% Coursework 1: 6 page report
- 25% Coursework 2: Demonstration of working AI model
- 25% Coursework 3: Construct and validate an AI model and 6 page report
- 25% Exam 1 (1-hour): Exam
Assessed by end of autumn semester
Educational Aims
To provide an introduction to the field of AI in the context of Electrical and Electronic Engineering.Learning Outcomes
LO1 Decide on appropriate strategies to counter the arguments against AI and the challenges of its application: privacy & data collection, ethics, bias and hallucinations for example.
LO2 Analyse the strengths and weaknesses of common algorithms by selecting the most appropriate solution for a specified problem.
LO3 Apply an existing pre-trained AI model to a specified problem.
LO4 Demonstrate proficiency in the data-driven modelling process from data collection and preparation through to model application and validation. (setup a machine learning project, organise data, apply of an appropriate model and visualise results).
This module contributes to the delivery of the following Engineering Council outcomes:
C3, M3, C4, C5, C6, M6, C8, C11, M11, C13, M13 and C17.