Credit points: 15
Quantitive analysis plays an important role in business analytics and knowledge engineering, thus it is very useful to develop computing skills for data regression and classification. This subject covers some fundamentals of computational intelligence techniques, including fuzzy inference systems, neural networks and hybrid neuro-fuzzy systems. The subject is designed with a focus on solving time-series forecasting problems using fuzzy inference systems, where fuzzy inference mechanisms and fuzzy rule extraction from numerical data are addressed. Some advanced learning techniques for training neural networks will also be highlighted. In labs and assignment students will work with business datasets for time-series prediction using a fuzzy system, which helps to consolidate the knowledge taught in the lectures and gain a hand-on experience on computational intelligence applications in business.
FacultyFaculty of Science, Tech & Engineering
Subject Co-ordinatorJustin Wang
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Prerequisites CSE2AIF or CSE2DBF
Incompatible subjects CSE4CI
Equivalent subjects INT3CI
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Artificial intelligence-a guide to intelligent systems.||Recommended||Negnevitsky, M.||ADDISON-WESLEY, 2002.|
|Readings||Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.||Recommended||Lin, C.T., Lee, C.S.||PRENTICE-HALL. 1996.|
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Melbourne, 2014, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorJustin Wang
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
|One 3-hour examination||70|
|One assignment report equiv. to 750 words||Hurdle requirement: In order to pass the unit, students must obtain an overall pass grade, pass the examination and pass the overall non-examination components.||30|