1. CIS 588 Neural Computing Course details

2. CIS 588 Neural Computing Course basics:  Instructor - Iren Valova  Tuesday, Thursday 5 - 6:15pm, T 101  1 midterm, 1 project, 1 presentation, 3 homeworks, Final  Fundamentals of Neural Networks, Laurene Fausett, Prentice Hall, 1995  Additional resources are found in the class web site.

3. Neural network - what is it? 1960s - neural network research preceded the digital computer, but dwindled in 1969 after Minsky and Papert 1986 - Rumelhart showed that multilayer perceptron could overcome the limitations described by Minsky Rumelhart popularized the notion that there are other viable architectures; by 1989 there were two societies as forum for NN research by 1991 people began to realize the significance of computers that could learn new things without having to be explicitly reprogrammed Learning means behaving better as a result of experience.

4. Neural network - what can I do with it? Why do I need it? with all the attention the NNs have received, there are still only a handful of commercially successful applications; many people have heard about NN, yet few have concept of how to apply them NN are exciting because the technology offers the promise of computer system that can dynamically adapt to new situations NN only require for the learning algorithm, input signals, and the set that collectively represents the desired behavior, to be specified the underlying concept is unlike any of the mainstream approaches and is essential for the successful application of NN

5. Neural network - Why do I need it? computers - biggest bang for the buck, inexpensive, reliable, and fast automation problems, NP problems, intractable problems (tasks people do extremely well, but difficult to model) brain - limited to operations in milliseconds, but working in parallel, self-organizing computers are sequential

6. Applications of Neural Networks Stocks, Commodities, and Futures Business, Management, and Finance Medical Applications Sports Applications Science Manufacturing Pattern Recognition

7. Stocks, Commodities, and Futures Forecasting Stock Prices – Determines if stock is being underpriced or overpriced by the market. Cost Prediction – Predicts the next month's gas price change.

8. Business, Management, and Finance Credit Scoring – Predicts loan application success Identifying Potential for Misconduct – Predicts misconduct potential based on employee records. Finding Gold – Recognizes gold deposits

9. Medical Applications Diagnosing Heart Attacks – Recognizes Acute Myocardial Infarction from enzyme data. Breast Cancer Cell Analysis – Image analysis ignores benign cells and classifies malignant cells.

10. Sports Applications Thoroughbred Horse Racing – Predicts the winning horse in a race. Dog Racing – Predicts the winning dog in a race.

11. Science Mosquito Identification – Recognizes two species and both sexes of mosquitoes. Weather Forecasting – Predicts both the probability and quantity of rain in a local area.

12. Manufacturing Welding Quality – Recognizes welds which are most likely to fail under stress. Computer Chip Manufacturing Quality – Analyzes chip failures to help improve yields. Beer Testing – Identifies the organic content of competitors' beer vapors.

13. Pattern Recognition Speech Recognition – Voice mail recognition for rotary phone systems. Classification of Text – Provides contextual information about text.

14. Reference BrainMaker Neural Network Software URL: www.calsci.com