CSE4CI
COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
CSE4CI
2016
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.
SchoolSchool Engineering&Mathematical Sciences
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, SGIT or SGCS.
Co-requisitesN/A
Incompatible subjects CSE3CI AND Students in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.
Equivalent subjectsN/A
Special conditionsN/A
Graduate capabilities & intended learning outcomes
01. Explain the technologies and applications of computational intelligence systems (CIS).
- Activities:
- Lecture 1 is on the introduction of computational intelligence systems and its applications.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Writing(Writing)
- Inquiry/ Research(Inquiry/ Research)
02. Explain the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks.
- Activities:
- 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, and supervised and unsupervised learning algorithms. Lecture 8 is on Genetic Algorithms for solving model or parameter optimization problems.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Creative Problem-solving(Creative Problem-solving)
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Inquiry/ Research(Inquiry/ Research)
03. Evaluate fusion technology, i.e. hybrid intelligent systems and the links between CIS and knowledge engineering.
- Activities:
- 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.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Creative Problem-solving(Creative Problem-solving)
04. Implement a fuzzy expert system and a neural network for time-series forecasting with real world data from industry.
- Activities:
- 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 are on neural network modeling and applications.
- Related graduate capabilities and elements:
- Quantitative Literacy/ Numeracy(Quantitative Literacy/ Numeracy)
- Ethical Awareness(Ethical Awareness)
- Critical Thinking(Critical Thinking)
- Teamwork(Teamwork)
- Creative Problem-solving(Creative Problem-solving)
- Inquiry/ Research(Inquiry/ Research)
- Discipline-specific GCs(Discipline-specific GCs)
Subject options
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Melbourne, 2016, 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 | % | ILO* |
|---|---|---|---|
| Assignment 1 - Fuzzy expert system design | 20 | 04 | |
| Assignment 2 - Neural networks modeling (Group project) | 20 | 04 | |
| Exam | 60 | 01, 02, 03 |