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Course Description
| CS 256 - Neural Computation | ||
| Instructor(s): Robert Snapp |
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| Description: Introduction to artificial neural networks, their computational capabilities and limitations, and the algorithms used to train them. Statistical capacity, convergence theorems, backpropagation, reinforcement learning, generalization. |
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| Prerequisites: Math 124 (or 271), Stat 153 or equivalent, computer programming. |
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| Methodologies: Pattern recognition/classification/data mining, Statistical modeling |
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| Domains: Not specific to any particular application domain |
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| Frequency: Not sure | ||
| Credits: 3 | ||
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