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

01. Describe the technologies and applications of computational intelligence systems (CIS).
02. Describe the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks.
03. Explain the fusion technology, i.e., hybrid intelligent systems and the links between CIS and knowledge engineering.
04. Implement a fuzzy expert system for time-series forecasting.

Subject options

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Start date between: and    Key dates

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 elementCommentsCategoryContributionHurdle% ILO*
One 3-hour examination Hurdle requirement: To pass the subject, a pass in the examination is mandatory.N/AN/AN/AYes70 SILO1, SILO2, SILO3
One assignment (1200 word equiv)N/AN/AN/ANo30 SILO4