Introduces students to intelligent decision systems used in organisations. We will primarily focus on expert systems (ES) and decision support systems (DSS). Topics include decisions and decision making, decision support systems and expert systems, development approaches, artificial neural networks, and some cutting edge intelligent technologies.
1. Decision support systems and business intelligence
2. AI and expert systems
3. Expert systems (ES): Development, justification and dependency diagrams
4. ES: Knowledge acquisition, representation, reasoning and chaining
5. ES: Uncertainty and confidence factors
6. Decision Support Systems (DSS) Overview
7. DSS modelling, analysis and business intelligence
8. Artificial neural networks (ANN)
9. Collaboration and knowledge management
10. Advanced IDS: Case Based Reasoning (CBR) and Genetic Algorithms (GA) & Fuzzy Logic
11. Advanced IDS: Intelligent Agents
12. Implementing DSS, BI and Integration, impacts & future issues
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
Learning outcomes and graduate attributes
|On completion of this unit, students should be able to:||GA1||GA2||GA3||GA4||GA5||GA6||GA7|
|1||identify and discuss different decision types and the process of decision making and their relevance to decision support||Intellectual rigour||Knowledge of a discipline||Lifelong learning|
|2||describe, discuss and compare the components, characteristics and uses of expert systems, especially the application of knowledge engineering principles to the creation of knowledge bases||Intellectual rigour||Knowledge of a discipline||Lifelong learning|
|3||identify and apply the appropriate technology, development methodology and current tools for both ES and DSS requirements||Intellectual rigour||Knowledge of a discipline||Lifelong learning|
|4||identify and discuss the application of neural networks, fuzzy logic and genetic algorithms to DSS||Intellectual rigour||Knowledge of a discipline||Lifelong learning|
|5||describe, discuss, and compare cutting-edge intelligent technologies and analyse problem situations to recommend which technology (or technologies) are best suited||Intellectual rigour||Knowledge of a discipline||Lifelong learning|
|6||describe and discuss implementation, societal and organisational impacts.||Intellectual rigour||Knowledge of a discipline||Lifelong learning||Cultural competence|
- No prescribed texts.
Teaching and assessment
|Lecture online 1 hour (12 weeks)|
|Exam: closed book||50%|
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Commencing 2015 Commonwealth Supported only. Student contribution band: 2
Please check the international course and fee list to determine the relevant fees.