Leader: Red Ceglowski
Clayton Second semester 2005 (Day)
Synopsis: Solving practical business problems using commercially available neural network software. Focuses on business applications, with discussion of suitable neural network architectures and convergence issues. Students gain hands-on experience with commercial neural network software packages, and solve real business problems in labs and assignments. Topics include principles and mechanisms in neural networks; perceptions for marketing and business data classification/analysis; multilayer feedforward neural networks for time series, stock market prediction and written character recognition; convergence issues of neural networks, data mining methodologies and artificial intelligence in business.
Assessment: Examination (2 hours): 50% + Practical work (assignment): 30% + Mid-semester test: 20% + Students must pass the examination in order to pass the unit
Contact Hours: One 2-hour lecture and one 1-hour laboratory per week
Prerequisites: BUS9530 or equivalent quantitative unit
Prohibitions: BUS3650, BUS4650