COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
Credit points: 15
Quantitative 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.
SchoolEngineering and Mathematical Sciences
Subject Co-ordinatorAndrew Skabar
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 3 - UG
Available as ElectiveNo
PrerequisitesCSE2DBF OR CSE2AIF
Incompatible subjects CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Minimum credit point requirementN/A
Artificial intelligence-a guide to intelligent systems.
Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.
AuthorLin, C.T., Lee, C.S.
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Intended Learning Outcomes
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Melbourne (Bundoora), 2021, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorJustin Wang
Laboratory ClassWeek: 11 - 22
One 2.00 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
LectureWeek: 10 - 22
One 2.00 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 Hurdle requirement: To pass the subject, a pass in the examination is mandatory.||N/A||N/A||Yes||70||SILO1, SILO2, SILO3|
|One assignment (1200 word equiv)||N/A||N/A||No||30||SILO4|