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CIS 588 Neural Computing

CIS 588 Neural Computing. Course details. 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

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CIS 588 Neural Computing

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  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

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