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Business applications of neural networks (6 points)


Leader: Kate Smith

Clayton First semester 2003 (Day)
Clayton First semester 2004 (Day)
Clayton Summer 2004 (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

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