cse3cix computational intelligence for data analytics

COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS

CSE3CIX

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 assignments 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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorJustin Wang

Available to Study Abroad/Exchange StudentsNo

Subject year levelYear Level 3 - UG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesCSE2DBX OR CSE2DCX
Students must be admitted in one of the following courses: SBAIO, SBACTO

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems.

Resource TypeBook

Resource RequirementRecommended

AuthorLin, C.T., Lee, C.S.

Year1996

Edition/VolumeN/A

PublisherPrentice-Hall

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Artificial intelligence-a guide to intelligent systems.

Resource TypeBook

Resource RequirementRecommended

AuthorNegnevitsky, M.

Year2002

Edition/VolumeN/A

PublisherAddison-Wesley

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Computational Intelligence for Data Analytics

Resource TypeBook

Resource RequirementRecommended

AuthorDidasko

Year2018

Edition/VolumeN/A

PublisherDidasko Digital

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Critically evaluate the technologies and applications of computational intelligence systems (CIS relating to business analytics and knowledge engineering.
02. Develop different computational intelligence systems, such as forecasting and classification systems using techniques which replicate human decision-making.
03. Compare and contrast computational intelligence techniques and knowledge engineering using different business applications.
04. Implement a decision support system for time-series forecasting to set business goals and objectives.

Subject options

Select to view your study options…

Start date between: and    Key dates

Online (Didasko), 2020, Study block 1, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 2 - 13
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 2 to week 13 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 10, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 41 - 52
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 41 to week 52 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 11, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 45 - 0
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 45 to week 0 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 12, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 49 - 0
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 49 to week 0 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 2, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 6 - 17
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 6 to week 17 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 3, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 10 - 21
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 10 to week 21 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 4, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 14 - 25
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 14 to week 25 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 5, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 19 - 30
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 19 to week 30 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 6, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 23 - 34
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 23 to week 34 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 7, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 27 - 38
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 27 to week 38 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 8, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 32 - 43
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 32 to week 43 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4

Online (Didasko), 2020, Study block 9, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorJustin Wang

Class requirements

Unscheduled Online ClassWeek: 36 - 47
One 3.00 hours unscheduled online class per week on any day including weekend during the day from week 36 to week 47 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle%ILO*

Online test (30 minutes) (equivalent to 500 words)Multiple-choice and/or short answer questions on computational intelligence systems. Test will be conducted in week 5.

N/AN/AN/ANo15SILO1

Assignment on computational intelligence system (equivalent to 1500 words)A practical scenario-based report on developing a computational intelligence system using fuzzy inference systems or neural networks or hybrid intelligent system

N/AN/AN/ANo30SILO2

Online test (60 minutes) (equivalent to 1000 words)Multiple-choice and/or short answer questions test that covers the theoretical knowledge

N/AN/AN/ANo25SILO2, SILO3

Assignment on time-series forecasting (equivalent to 1500 words)A practical scenario-based report on using a tool to implement a fuzzy expert system for resolving a time-series forecasting problem.

N/AN/AN/ANo30SILO4