I took this class taught by Charles Isbell in spring 2007 at the Georiga Institute of Technology.
I did 4 assignments and the final project. You can find a brief description of each underneath as well as the source code and the analysis.
I did this project together with Adebola Osuntogu and Mingxuan Sun. We won the bestpaper award that the other students could vote for.
Given some images of buildings and nonbuilding our goals are:
 discriminate buildings from nonbuildings
 recognize a specific building
We compare three different
algorithms: Consistent Line Clusters(CLC), Randomized Decision Trees and
the Vocabulary Tree.To compare our results to different authors we test our algorithms for the first problem on a subset of the Caltech256 dataset. The second problem is tested on the ZuBuD dataset.
We obtain 99% accuracy on the building versus nonbuilding
classification and 75.6% on specific building classification. We conclude that the
CLC method performs best in first case, while the Vocabulary Tree performs best
in the second case, although Randomized Trees perform similar (72%).
More details can be found in our paper and our slides.



CLC overlayed
over building 
Pixel locations for
randomized tree

MSER features overlayed
over building

[ paper  slides ]
We had to evaluate 5 different machine learning algorithms on 2 datasets. The algorithms are:
 Decision trees with some form of pruning
 Neural networks
 Boosting
 Support Vector Machines
 kNearest neighbors
I used two datasets for evaluation of the 5 machine learning algorithms, one set of images of
hand written digits and one set of cell nucleus properties to enhance breast tumor diagnosis.



Digit sample 
Snakes fitted around
cell nucleus 
Search for optimal
SVM parameters 
More details can be found in the analysis.
[ analysis  source ]
We had to evaluate 4 different optimization techniques on several optimization problems. The optimization techniques are:
 randomized hill climbing
 simulated annealing
 a genetic algorithm
 MIMIC



Rastriginâ€™s function 
Solutions found by
the genetic algorithm
for rastrigin's function 
TSP solved by genetic algorithm 
More details can be found in the analysis.
[ analysis  source ]
We had to compare two clustering algorithms
 kmeans clustering
 Expectation Maximization
and four dimension reduction techniques:
 PCA
 ICA
 Randomized Projections
 naive dimension reduction via downsampling and mean intensity
on two datasets. I reused the datasets from assignment 1.



First eigenimage
for digit datset 
5th indpendent image
for digit dataset 
digit 9 projected
on the first 319 random
dimensions 
More details can be found in the analysis.
[ analysis  source ]
We had to compare policy and value interation on two interesting Markov Decision Processes. I took a process with only a few states (a grid world) and one with many states (car racing example).



Solution for easy
gridworld 
Solution for 20x20 grid 
Solution for the hard
car track 
More details can be found in the analysis.
[ analysis  source ]
