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
Aims and Objectives
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The main techniques that have been used in AI, and their range of applicability
- Basic differential and integral calculus
- Likely future developments in AI
- The difficulty of distinguishing AI from advanced computer science in general
- The principal achievements and shortcomings of AI
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Assess the claims of AI practitioners as they relate to `intelligence'
- Assess the validity of approaches to model intelligent processing
- Select appropriately from a range of techniques for intelligent systems
- Assess the applicability of AI techniques in novel domains
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.
- Differentiation - standard rules; Newton's method for finding roots; partial differentiation; integration - standard integrals; integration by parts; numerical integration.
- 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*.
- 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.
- 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
|Completion of assessment task||15|
|Wider reading or practice||43|
|Preparation for scheduled sessions||18|
|Total study time||150|
Resources & Reading list
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
Copeland J, (1993). Artificial Intelligence: A Philosophical Introduction. Blackwell.
Russell, S and Norvig, P (2003). Artificial Intelligence: A Modern Approach. Prentice Hall.
Nilsson NJ (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann.
Mitchell, T. (1997). Machine Learning. McGraw-Hill.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
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.
Repeat type: Internal & External