The aim of this module is to equip students with the specialist knowledge, understanding and skills to address the canonical problems (supervised, semi-supervised and reinforcement learning) related to the use of artificial intelligence which can be applied to develop user-facing solutions and understand human behaviours in various domains
By the end of this module the student should be able to:
1. Describe the range of ML and AI methods and understand their assumptions and application/operating constraints
2. Evaluate and select an optimal ML/AI technique to develop a user-facing AI solution or study human behaviours in a specific context
3. Use an appropriate software application to apply an ML technique to a given problem and communicate the findings and significance
1 Machine Learning Concepts
Intro to AI/ML landscape
2 General Model Assessment and Selection
How do we measure the accuracy or strength of an AI?
Given this input from a dataset, what is the likely value of a particular quantity?
Which data points are similar to each other?
5 Dimensionality Reduction
What are the most significant features of this data and how can these be summarised?
To which category does this data point belong?
7 Artificial Neural Networks
How do artificial neural networks work?
8 Semi-supervised Learning
How can labelled and unlabelled data be combined?
9 Reinforcement Learning
What actions will most effectively achieve a desired endpoint?
10 Explainable AI
Discussing emerging research on Explainable AI (XAI).
Teaching and Learning Work Loads
|Teaching and Learning Method||Hours|
SCQF Level - The Scottish Credit and Qualifications Framework provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.
Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.
We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2021/22 , and may be subject to change for future years.