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EE52036: Artificial intelligence and machine learning for engineering and design

[Page last updated: 16 August 2024]

Academic Year: 2024/25
Owning Department/School: Department of Electronic & Electrical Engineering
Credits: 20 [equivalent to 40 CATS credits]
Notional Study Hours: 400
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 1
Assessment Summary: CWRI 50%, EXOB 50%
Assessment Detail:
  • AI/ML for engineering and design examination (EXOB 50%)
  • AI/ML for engineering and design coursework (CWRI 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Learning Outcomes: Apply appropriate machine learning algorithms and analyse complex data systems in an engineering and design context
Critically evaluate the merits and limitations of different machine learning methods.
Demonstrate teamworking competencies and evaluate effectiveness of own and team performance.
Produce practical engineering solutions for complex problems using AI/ML tools and critically evaluate and present the outcomes.
Recognise and describe the machine learning pipeline of training, testing, and deploying ML.


Synopsis: Working individually and in teams, you'll use software tools to learn core AI and ML methods such as supervised and unsupervised learning, neural networks and deep learning. You'll explore and apply AI and ML workflows to prepare, process, and analyse data. From this, you'll develop creative solutions to complex engineering and design challenges.

Aims: Using state-of-the-art software tools and working individually and in teams you will develop your understanding of core AI and ML methods including supervised and unsupervised learning, neural networks and deep learning. Explore and implement AI and ML workflows to prepare, process, and analyse data to creatively develop cutting-edge solutions for engineering and design complex challenges.

Skills: Data preparation, data collection, preparation, model training and validation, interpretation of results.

Content:
  • Introduction to machine learning concepts
  • Model evaluation and comparison
  • Machine learning workflows including data collection, preparation, model training and validation, interpretation of results.
  • Traditional machine learning approaches (e.g. principle component analysis, k-means clustering and decision trees) and their application to optimisation, regression and classification
  • Artificial neural networks and deep learning ( e.g. perceptron, convolutional neural networks ) and their applications
  • Introduction to more advanced techniques such as Bayesian ML and hidden Markov models
  • Software and tools for machine learning
  • Sustainability implications of AI
  • Case studies


Course availability:

EE52036 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.