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 regression and classification systems using machine learning and neural networks; Explain the learning technology, i.e., intelligent systems and the links between CIS and knowledge engineering; Implement a neural network system for data 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 classical learning and inference systems, neural networks, and reinforcement learning systems. The subject is designed with a focus on solving forecasting problems using learning and inference systems, where learning mechanisms and learning 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 real- world datasets for prediction using a learning system, which helps to consolidate the knowledge taught in the lectures and gain a hands-on experience on computational intelligence applications in practice.