COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS
Credit points: 15
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.
SchoolSchool Engineering&Mathematical Sciences
Subject Co-ordinatorJustin Wang
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Prerequisites CSE2AIF or CSE2DBF
Incompatible subjects CSE4CI AND students enrolled in any Graduate Diploma or Masters by Coursework course.
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Artificial intelligence-a guide to intelligent systems.||Recommended||Negnevitsky, M.||ADDISON-WESLEY, 2002.|
|Readings||Neural fuzzy systems-a neuro-fuzzy synergism to intelligent systems.||Recommended||Lin, C.T., Lee, C.S.||PRENTICE-HALL. 1996.|
Graduate capabilities & intended learning outcomes
01. Describe the technologies and applications of computational intelligence systems (CIS).
- Lecture 1 is on the introduction of computational intelligence systems and its applications.
02. Describe the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks.
- 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, supervised and unsupervised learning algorithms. Lecture 8 is on Genetic Algorithms for solving model or parameter optimization problems.
03. Explain the fusion technology, i.e., hybrid intelligent systems and the links between CIS and knowledge engineering.
- 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.
04. Implement a fuzzy expert system for time-series forecasting.
- 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 on neural network modeling and applications.
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Melbourne, 2019, Semester 1, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorJustin Wang
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
|One 3-hour examination||Hurdle requirement: To pass the subject, a pass in the examination is mandatory.||70||01, 02, 03|
|One assignment (1200 word equiv)||30||04|