CIS 588 Neural Computing Course details CIS 588

CIS 588 Neural Computing Course details

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

Neural network - what is it? • 1960 s - 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.

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

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

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

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

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

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

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

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

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

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

Reference Brain. Maker Neural Network Software URL: www. calsci. com