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| Unit name |
Intelligent Information Systems |
| Unit code |
EMATM0042 |
| Credit points |
10 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Professor. Liu |
| Open unit status |
Not open |
| Pre-requisites |
EMAT31530, EMATM1120
|
| Co-requisites |
None
|
| School/department |
School of Engineering Mathematics and Technology |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
This unit will introduce AI techniques for knowledge representation, information processing, fusion and decision making. It will adopt and application centred approach aimed at giving students experience at designing information systems. It also aims to provide students with a background in the following key areas:
- Knowledge Representation, including a brief overview of first order logic, probabilistic logic, semantic networks, and event reasoning under uncertainty (event representation, event correlation and event reasoning).
- Agent-based models for developing intelligent autonomous systems such as Belief–desire–intention models, or Markov Decision Process (MDPs).
- Handling inconsistency in knowledge
- Information fusion under uncertainty approaches and their comparisons, and applications
- Real-world application scenarios: covering how to scope a problem/scenario, how to elicit domain knowledge, how to identify data items, how to develop a data-driven intelligent system given a specific real-world problem.
Intended Learning Outcomes
- Demonstrate an understanding of modern information modelling frameworks, information sources and related applications.
- Be able to apply simple core knowledge representation, reasoning and decision making principles.
- Be able to explain the importance of information processing in real-world applications.
- Demonstrate an understanding of core design requirements for intelligent information systems, including multi-agent systems.
Teaching Information
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, supported by live online sessions, problem sheets and self-directed exercises.
Assessment Information
1 Summative Assessment, 100% - Coursework. This will assess all ILOs.
Reading and References
None
