1. Neural Networks My name is Burleson

2. Neural Networks vs Conventional Computing Programming is broken into small, unambiguous steps Algorithms must be already known and understood in order to obtain a result Errors in results are always software or hardware based Neural networks are trained Can obtain results when there is no “ solution ” Errors are based on faulty training data sets

3. General Structure Composed of nodes arranged in layers (or evil lairs, depending) Each node can take different input or the same input and output various results Unique patterns among nodes determine an end result of the execution Back propagation can allow a network to train itself by giving feedback to each node

4. Three Types of Learning Supervised learning involves a human training the network repeatedly in order to improve accuracy (military facial recognition) Unsupervised learning can be done by the computer where certain cost functions aim to be minimized (image compression) Reinforcement learning is based on the computer building its own data and extracts reliable knowledge based on repetition of events (game intelligence)

5. Potential Problems Over training can lead to the network not being able to identify new input correctly if the network it too dependant on the original training data Generalization can lead to false positives if the training set was too diverse or the error correction was not strict enough Error correction is touchy - steps can be taken in large or small increments with smaller steps being more accurate but slower and larger steps doing the other (like, duh, for sure)

6. Explanation of a Simple Network I have no idea, but luckily for us, this site can explain (please pull out your binoculars for the remainder of the presentation): http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#An%20engineering%20approach

7. Interesting Sites http://rodin.wustl.edu/cosc/doc3.html http://www.20q.net/