Part II: Mechanics & Application
Make AI work for you
A 4-Class Module on AI Systems, Mechanisms, and Behaviour
What This Module Is
Most introductions to AI focus on outputs.
This module focuses on process.
We look inside AI systems to understand:
- how they represent information
- how they generate outputs
- how structural design choices shape behaviour
AI systems are not magic. They are pipelines of mechanisms.
Once you see the structure, you can begin to reason about:
- what the system does well
- where it fails
- and why.
The Arc
| Class | Title | What Happens | What You Learn |
|---|---|---|---|
| 1 | Machine Learning 101 | We translate real problems into AI workflows | Basic lay of the land and key principles of machine learning |
| 2 | Neural Network & Generative Modelling | We examine how models transform inputs into outputs | Neural networks, extensions (images and graphs), and the role of probability & randomness in generative modelling |
| 3 | Extending LLMs | We examine how LLMs can be augmented into powerful agents, and supported course project development | Components and principles of LLM-powered applications |
What We’re Asking You to Do
In this module, you are asked to shift perspective.
Not from user → output.
But from system → mechanism → behaviour.
Your task is to:
-
See the pipeline.
Understand how an AI system breaks a problem into computational steps. -
Locate the mechanism.
Identify the component that actually produces behaviour. -
Explain the behaviour.
Show how structural design decisions create strengths, limits, and failure patterns.
Learning Outcomes
By the end of this module, you will be able to:
- Translate real-world problems into AI system pipelines
- Identify and explain mechanisms within AI systems
- Analyse how inputs → transformations → outputs produce observable behaviour
- Reason about how mechanisms, datasets, and responsibility considerations interact
- Communicate system behaviour through clear diagrams and structured explanation
How You’ll Be Assessed
This module is assessed through A1.2 — Case Study 2: The Revealing Mechanism, an individual case study focused on explaining how a specific AI mechanism works.
You will analyse an AI system and show:
- what mechanism it uses
- what problem the mechanism solves
- how it shapes system behaviour
Full requirements and deliverables are provided in the assignment-specific outline [link].
This should be read alongside the general case study rubric [link].
Resources
- Reading Material: see Part II: Mechanics of AI under [link].
The One-Liner
“If you can’t explain the mechanism, you don’t understand the system — and you can’t make AI work for you.”