COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
CSE5CI
Not currently offered
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 linear regression analysis, 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 and neural networks with advanced learning algorithms, which help to consolidate the knowledge taught in the lectures and gain a hands-on experience on computational intelligence applications in business.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorJustin Wang
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
Prerequisites CSE4DBF or admission into one of the following courses: SMIT, SMICT,SMITCN SMCSC, SGIT, SGCS or SMDS
Co-requisitesN/A
Incompatible subjectsCSE3CI OR CSE4CI
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsStudents in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.
Minimum credit point requirementN/A
Assumed knowledgeN/A
Learning resources
Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems
Resource TypeBook
Resource RequirementRecommended
AuthorC. T. Lin, C. S. Lee
Year1996
Edition/VolumeN/A
PublisherPrentice-Hall
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Artificial intelligence-a guide to intelligent systems
Resource TypeOther resource
Resource RequirementPrereading
AuthorM. Negnevitsky
Year2011
Edition/Volume3 edition
PublisherAddison Wesley
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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