AI Lab4: Neural Networks

Due Monday May 5th at midnight. Paper due under my door by Wednesday May 7th at noon.

Program:

Build a simple three layer Neural Neural network that learns through back-propagation.

consider the following inputs and target outputs:

MO:  1    1    0    0    1    1    1    0    1    1    0
NM:  1    0    0    0    1    1    1    1    1    1    0
MN:  0    0    0    0    0    0    0    0    1    0    0
OH:  1    1     0    0    1    1    1    0    1    1    0
FL:   1    1     0    1    1    1    1    0    1    1    0
VA:  1    1     1    1    1    1    1    1    1    1    0
PA:   0    0    0    0    1    1    1    0    1     0    0
NH:  0    1    0    0    1    1    1    1    1     1    0

OUT: 1    1    0    0    1    1    1    0    1    1    0

Make your network have no more than 4 hidden layers. First train the network on the first 4 sets of input and output. When you are satisfied that your network has learned this limited training data, test it on the next four examples. How did it do? Explain that in your papger. Now have it learn from scratch using all but the first example. (throw away your earlier weights). Once the network is fully trained, test it on the first (leftmost) example above. How did your network do? Mention its performance and discuss in your paper.

Paper:

Write a paper discussing the merits of neural networks using Backpropagation as a learning technique. As usual include an analys of how intelligent the technique appears to be, how flexible and the rest.

paper outline. Have at least this in your paper. You are encouraged to cover more if you find something interesting about your program or you have some other insite into the technique

Introduction: What is this technique and what class of problems does it solve
Discussion of the technique. Where is the intelligence? How much intelligence is there. What sorts of applications would you use this technique for
Discussion of your implementation: What interesting things did you see in your own implimentation of neural nets
Summary: what classes of problems should this technique solve and in your experience, what would they solve?