cse4ci computational intelligence
COMPUTATIONAL INTELLIGENCE
CSE4CI
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 and neural networks with advanced learning algorithms, which help 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 4 - UG/Hons/1st Yr PG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites CSE2AIF or CSE2DBF or equivalent AND Enrolment in one of the following courses: SMIT, SMICT,SMITCN SMCSC, SMBBS, SGBBS, SGCS, SGIT or SGCS.
Co-requisitesN/A
Incompatible subjects CSE3CI
Equivalent subjectsN/A
Special conditionsN/A
Subject options
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Melbourne, 2014, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorJustin Wang
Class requirements
Laboratory ClassWeek: 10 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
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.
Assessments
Assessment element | Comments | % |
---|---|---|
Assignment 1 - Fuzzy expert system design | 20 | |
Assignment 2 - Neural networks modeling (Group project) | 20 | |
Exam | 60 |