cse3ci computational intelligence




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.

SchoolSchool Engineering&Mathematical Sciences

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


Incompatible subjects CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course.

Equivalent subjectsN/A

Special conditionsN/A

Learning resources


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.

Graduate capabilities & intended learning outcomes

01. Describe the technologies and applications of computational intelligence systems (CIS).

Lecture 1 is on the introduction of computational intelligence systems and its applications.

02. Describe the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks.

Lectures 2, 3, 4 are on fuzzy logic and fuzzy inference systems. Lectures 5, 6, 7 are on some basics of neural networks, including neuron models, supervised and unsupervised learning algorithms. Lecture 8 is on Genetic Algorithms for solving model or parameter optimization problems.

03. Explain the fusion technology, i.e., hybrid intelligent systems and the links between CIS and knowledge engineering.

Lectures 9 and 10 are on hybrid intelligent system design. Lecture 11 is on knowledge engineering, where students will find the links between computational intelligence techniques and data mining and knowledge engineering for their further studies.

04. Implement a fuzzy expert system for time-series forecasting.

Lab 1 to Lab 6 are on fuzzy expert system implementation, where students will learn how to use Matlab tools to implement a computational intelligence system for resolving a time-series forecasting problem. Lab 7 and Lab 8 on neural network modeling and applications.

Subject options

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Start date between: and    Key dates

Melbourne, 2019, Semester 1, Day


Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJustin Wang

Class requirements

LectureWeek: 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.

Laboratory ClassWeek: 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.


Assessment elementComments%ILO*
One 3-hour examinationHurdle requirement: To pass the subject, a pass in the examination is mandatory.7001, 02, 03
One assignment (1200 word equiv)3004