COMPUTATIONAL INTELLIGENCE FOR DATA ANALYTICS (4 SCU)

Learning Outcomes

Describe the technologies and applications of computational intelligence systems (CIS); Describe the major components and issues in developing computational intelligence systems, such as forecasting and classification systems using fuzzy inference and neural networks; Explain the fusion technology, i.e., hybrid intelligent systems and the links between CIS and knowledge engineering; Implement a fuzzy expert system for time-series forecasting.

Topics

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.

Prerequisite(s): None