COMPUTATIONAL INTELLIGENCE

CSE3CI

2014

Credit points: 15

Subject outline

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

Credit points15

Subject Co-ordinatorJustin Wang

Available to Study Abroad StudentsYes

Subject year levelYear Level 3 - UG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE2AIF or CSE2DBF

Co-requisitesN/A

Incompatible subjects CSE4CI

Equivalent subjects INT3CI

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsArtificial intelligence-a guide to intelligent systems.RecommendedNegnevitsky, M.ADDISON-WESLEY, 2002.
ReadingsNeural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.RecommendedLin, C.T., Lee, C.S.PRENTICE-HALL. 1996.

Subject options

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Start date between: and    Key dates

Melbourne, 2014, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJustin Wang

Class requirements

Laboratory Class Week: 11 - 22
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.

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments%
One 3-hour examination70
One assignment report equiv. to 750 wordsHurdle 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