COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
CSE3CI
2021
Credit points: 15
Subject outline
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
Credit points15
Subject Co-ordinatorAndrew Skabar
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 3 - UG
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
PrerequisitesCSE2DBF OR CSE2AIF
Co-requisitesN/A
Incompatible subjects CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsN/A
Minimum credit point requirementN/A
Assumed knowledgeN/A
Readings
Artificial intelligence-a guide to intelligent systems.
Resource TypeRecommended
Resource RequirementN/A
AuthorNegnevitsky, M.
Year2002
Edition/VolumeN/A
PublisherADDISON-WESLEY
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.
Resource TypeRecommended
Resource RequirementN/A
AuthorLin, C.T., Lee, C.S.
Year1996
Edition/VolumeN/A
PublisherPRENTICE-HALL.
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Career Ready
Career-focusedNo
Work-based learningNo
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
Graduate Capabilities
Intended Learning Outcomes
Subject options
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Melbourne (Bundoora), 2021, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorJustin Wang
Class requirements
Laboratory ClassWeek: 11 - 22
One 2.00 h 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 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
| 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 |