Module overview
This module aims to give a broad introduction to the rapidly-developing field 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:
- The principal achievements and shortcomings of AI
- The difficulty of distinguishing AI from advanced computer science in general
- Likely future developments in AI
- The main techniques that have been used in AI, and their range of applicability
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Assess the validity of approaches to model intelligent processing
- Select appropriately from a range of techniques for intelligent systems
- Assess the ethical issues of intelligent systems.
- Assess the applicability of AI techniques to novel domains
- Assess the claims of AI practitioners as they relate to 'intelligence'
Syllabus
Introduction to AI
- Flavours of AI: strong and weak, neat and scruffy, symbolic and sub-symbolic, knowledge-based and data-driven.
- The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.
Search
- Finding satisfactory paths and outcomes; chosen from: depth-first and breadth-first, iterative deepening, evolutionary algorithms, hill-climbing and gradient descent, beam search and best-first. Finding optimal paths: branch and bound, dynamic programming, A*.
Representing Knowledge
- Production rules, monotonic and non-monotonic logics, semantic nets, frames and scripts, description logics.
Reasoning and Control
- Data-driven and goal-driven reasoning, AND/OR graphs, truth-maintenance systems, abduction and uncertainty.
Reasoning under Uncertainty
- Probabilities, conditional independence, causality, Bayesian networks, noisy-OR, d-separation, belief propagation.
Machine Learning
- Inductive and deductive learning, unsupervised and supervised learning, reinforcement learning, concept learning from examples, Quinlan's ID3, classification and regression trees, Bayesian methods.
Key Application Areas, selected from:
- Expert system, decision support systems
- Speech and vision
- Natural language processing
- Information Retrieval
- Semantic Web
Learning and Teaching
Teaching and learning methods
The content of this module is delivered through lectures, the module website, directed reading, pre-recorded materials and tutorials.
Students work on their understanding through a combination of independent study, preparation for timetabled activities and tutorials, along with formative assessments in the form of coursework assignments.
| Type | Hours |
|---|---|
| Lecture | 36 |
| Revision | 12 |
| Wider reading or practice | 25 |
| Preparation for scheduled sessions | 6 |
| Completion of assessment task | 41 |
| Tutorial | 12 |
| Follow-up work | 18 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Russell, S and Norvig, P (2003). Artificial Intelligence: A Modern Approach. Prentice Hall.
Mitchell, T. (1997). Machine Learning. McGraw Hill.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman.
Nilsson NJ (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufman.
Copeland J (1993). Artificial Intelligence: A Philosophical Introduction. Blackwell.
Assessment
Assessment strategy
This module is assessed by a combination of coursework and a final assessment in the form of a written examination.Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 40% |
| Examination | 60% |