COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
CSE3CI
2020
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.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Andrew Skabar
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 3 - UG
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE2DBF OR CSE2AIF
Co-requisites: N/A
Incompatible subjects: CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: N/A
Minimum credit point requirement: N/A
Assumed knowledge: N/A
Learning resources
Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.
Resource Type: Book
Resource Requirement: Recommended
Author: Lin, C.T., Lee, C.S.
Year: 1996
Edition/Volume: N/A
Publisher: PRENTICE-HALL.
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Artificial intelligence-a guide to intelligent systems.
Resource Type: Book
Resource Requirement: Recommended
Author: Negnevitsky, M.
Year: 2002
Edition/Volume: N/A
Publisher: ADDISON-WESLEY
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Career Ready
Career-focused: No
Work-based learning: No
Self sourced or Uni sourced: N/A
Entire subject or partial subject: N/A
Total hours/days required: N/A
Location of WBL activity (region): N/A
WBL addtional requirements: N/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Melbourne (Bundoora), 2020, Semester 1, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Justin Wang
Class requirements
Laboratory ClassWeek: 11 - 22
One 2.00 hours 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 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
One 3-hour examinationHurdle requirement: To pass the subject, a pass in the examination is mandatory. | N/A | N/A | Yes | 70 | SILO1, SILO2, SILO3 |
One assignment (1200 word equiv) | N/A | N/A | No | 30 | SILO4 |