Module overview
In this module, you will explore modern statistical learning and machine learning methods underpinning the recent AI revolution. The focus is on understanding how these methods work and the concepts that they use. While the methods depend on advanced mathematics, we will provide an overview that helps you understand the ideas behind them without needing a background in mathematics or computer science.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- understand the key concepts of supervised versus unsupervised learning;
- explain the difference between training, validation and test data sets, and their different roles in the learning process;
- understand the basic principles and structures of deep learning and large language models.
- identify the most common forms of data used in AI applications and the different challenges in using them;
- describe the trade-offs between (i) over- and under-fitting through the concepts of variance and bias, and (ii) accuracy and interpretability and the concept of explainable AI;
Syllabus
Typically:
Algorithms and their importance to Artificial Intelligence
Types of Artificial Intelligence algorithms
Supervised learning
- How supervised learning works
- Selection and applications of training data
- Concepts of classification, including different error types (e.g., sensitivity and specificity)
Unsupervised learning
- How unsupervised learning works
- Concepts of clustering
Reinforcement learning
- Concepts of model, policy, and value-based algorithms
Large Language Models
- Understand the principles of LLMs such as ChatGPT
Computer hardware and software requirements for AI technologies
Learning and Teaching
Teaching and learning methods
The programme employs a range of teaching and learning methods tailored to online delivery and the needs of working professionals. One of the primary methods used is asynchronous learning, where students can access materials on their own schedule. This includes multimedia resources - but not just video lectures, but also podcasts, animations, and interactive simulations; and reading materials like PDFs or e-books. These resources allow learners to engage with content at their own pace. In addition, discussion forums provide a space for students to ask questions and participate in debates with their peers without the need for everyone to be online at the same time. The asynchronous learning is complemented by synchronous components, such as webinars. These sessions, typically held via Microsoft Teams, give students the opportunity to interact with instructors in real-time, asking questions or participating in discussions. All of these methods are designed to accommodate different learning approaches and ensure that students can apply theoretical knowledge to practical scenarios relevant to their professional contexts. With a strong emphasis on self-paced learning, supported by ongoing instructor guidance.
| Type | Hours |
|---|---|
| Independent Study | 90 |
| Online Course | 26 |
| Guided independent study | 34 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Textbooks
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2023). An introduction to statistical learning. New York: Springer.
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat Information
Repeat type: Internal & External