This unit is offered as an introductory subject in intelligent systems for students in the Master of Information Technology Systems course with the Network Computing major.

The assessment for this unit has been changed from 3-hour examination (70%) and in-semester assessment (30%) to 2-hour examination (60%) and in-semester assessment (40%) in accordance with new faculty procedures. The change is to be implemented from Semester 1, 2017.

6 September 2018. Unit Learning Outcomes updated by CE from objectives to outcomes as per ACA requirements. ULO numbered rather than bullets.

24/9/2019: Admin - adding 10 minutes reading time to the overall exam duration as per University requirements.

19/11/2020: Admin - this unit was disestablished at FEC 5/20 on 12/11/2020 as it is no longer required.

This unit introduces main techniques widely used in intelligent software systems. With focus on network related techniques and modelling, the topics covered in this unit are relevant to students with the Network Computing major, thus complementing their degree.

On successful completion of this unit, students should be able to:

1. Explain the principles and theoretical underpinning of intelligent systems
2. Develop intelligent models and conduct experiments for solving a specific problem
3. Describe and evaluate the advantages and limitations of intelligent models and techniques for solving a wide range of practical problems
4. Analyse and evaluate current research developments in intelligent systems

020199

This unit introduces main techniques widely used in intelligent software systems to students in the Master of Information Technology Systems course with the Network Computing major. Specifically, it focuses on the techniques in relation to network structures. Main topics covered include neural network models, supervised learning and classification, unsupervised learning and clustering, fuzzy logic, intelligent decision analysis, optimum network flow modelling, and recommender systems.

On-campus

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

FIT