Module overview
This module aims to give a broad introduction to the rapidly-developing field of artificial intelligence, and to cover the mathematical techniques used by this module and by other artificial intelligence modules in the computer science programme
Linked modules
Pre-requisite: COMP1202
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The difficulty of distinguishing AI from advanced computer science in general
- Likely future developments in AI
- The principal achievements and shortcomings of AI
- The main techniques that have been used in AI, and their range of applicability
- Basic differential and integral calculus
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
- Assess the claims of AI practitioners as they relate to `intelligence'
- Select appropriately from a range of techniques for intelligent systems
- Assess the applicability of AI techniques in novel domains
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.
Calculus
- Differentiation - standard rules; Newton's method for finding roots; partial differentiation; integration - standard integrals; integration by parts; numerical integration.
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
Type | Hours |
---|---|
Completion of assessment task | 15 |
Wider reading or practice | 43 |
Preparation for scheduled sessions | 18 |
Revision | 20 |
Follow-up work | 18 |
Lecture | 36 |
Total study time | 150 |
Resources & Reading list
Textbooks
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
Copeland J, (1993). Artificial Intelligence: A Philosophical Introduction. Blackwell.
Nilsson NJ (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
Mitchell, T. (1997). Machine Learning. McGraw-Hill.
Russell, S and Norvig, P (2003). Artificial Intelligence: A Modern Approach. Prentice Hall.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Continuous Assessment | 20% |
Final Assessment | 80% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Set Task | 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 |
---|---|
Set Task | 100% |
Repeat Information
Repeat type: Internal & External