Module overview
This module introduces the fundamental concepts of machine learning and artificial intelligence. The content covers a broad range across the history of AI using computational, representational, algorithmic, and philosophical perspectives. The module looks at machine intelligence and machine learning as a fundamental attribute of artificial intelligence.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Philosophical basis of artificial intelligence and learning machines
- Ethical issues in artificial intelligence
- Achievements and limitations of different approaches to machine learning and artificial intelligence
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Implement and assess simple machine learning algorithms for classification or regression
- Perform reasoning with first order logic
- Create a simple game playing algorithm
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Describe the relationship of games to artificial intelligence
- Describe and discuss machine learning approaches for solving particular tasks
Syllabus
Philosophy of artificial intelligence
- Classifications of AI
- Fundamental problems in AI
Data representations
- Symbolic, sub-symbolic, abstract
Knowledge representations
- First order logic
- Taxonomies and ontologies
- Frames and schema
Machine learning
- Rule-based learning
- Linear classification
- Regression
- Statistical methods
Games and AI
- Early AI games
- Tree search
- State-action pairs
AI search paths
- Depth-first vs breadth-first
- Iterative deepening
- Search tree pruning
- Optimal search paths
Ethical issues in AI
- Bias in data
- Responsible research innovation
- Open problems
Learning and Teaching
Teaching and learning methods
The module consists of:
- Lectures
- Laboratory sessions
| Type | Hours |
|---|---|
| Specialist Laboratory | 6 |
| Guided independent study | 45 |
| Revision | 28 |
| Completion of assessment task | 35 |
| Lecture | 36 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Computing assignment | 20% |
| Exam | 50% |
| Class Test | 10% |
| Computing assignment | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 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 |
|---|---|
| Exam | 100% |
Repeat Information
Repeat type: Internal & External