COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
CSE5CI
Not currently offered
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 linear regression analysis, 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 hands-on experience on computational intelligence applications in business.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Justin Wang
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 5 - Masters
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE4DBF or admission into one of the following courses: SMIT, SMICT,SMITCN SMCSC, SGIT, SGCS or SMDS
Co-requisites: N/A
Incompatible subjects: CSE3CI OR CSE4CI
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: Students in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.
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: C. T. Lin, C. S. Lee
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: Other resource
Resource Requirement: Prereading
Author: M. Negnevitsky
Year: 2011
Edition/Volume: 3 edition
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