school work

I'll have a few manly project posts soon (working on a bridge and repairing a trailer), but much of my time is spent on school lately.  I can't post the code (since it could be used by other students and would constitute an honor code violation), but I can show some of the output.

A brief summary of the output of our AI assignments (for GaTech's OMSCS CS6601):

Assignment 5

On k-means clustering and expectation-maximization.  Required us to do k-means clustering implementation and use it to find ideal "average colors" and the groups of pixels that were closest to those averages.  It is an iterative routine which progressively changes which colors it chooses, and (consequently) which pixels are nearest in color to them.

Starting from this image:

This is what it looks like with 2 means (2 colors, and the pixels closest to them), then 3 means, 4, 5, and 6.

There were some other much trickier pieces, but they were much less visually pleasing.

Assignment 4

Is hard to visualize.  It was about automagically building random forests of decision trees to label samples of data.  We were given large amounts of data to build the model, and our models were tested against other unknown (but comparable) data.  Fitting well without overfitting was key.

Assignment 3

Was about modeling bayes nets and monte carlo markov chains.  It was very hard.

Assignment 2

Was about search - we implemented Uniform Cost Search (Dijkstra), A*, bidirectional, tridirectional and some other combinations.  In so doing we found good paths through a network like this:

Assignment 1

We wrote a game player using minimax with some enhancements to play a game that was a combination of


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