Software. Machine Learning. Ultimate.

About Me

Software engineer, manager, enthusiast.

Saving the planet from global warming through software products in the renewable energy sector.

Lifelong learner pursuing a Master's degree in Computer Science through Georgia Tech's OMS CS program.

Ultimate Frisbee player, trainer, coach.


Temporal Difference Learning

Replication of experiments from Richard S. Sutton’s 1988 paper on Temporal Difference Learning.

Reinforcement Learning

Two Markov Decision Processes of varying complexity were used to analyze value iteration, policy iteration and finally q-learning.

Unsupervised Learning

Two clustering algorithms (k-means, expecation maximization) and four dimensionality reduction algorithms (PCA, ICA, Randomized Projections, Information Gain) were implemented and explored.

Randomized Optimization

The report takes a closer look at random optimization (random hill climbing, genetic algorithm, simulated annealing) with the previously used datasets as well as some classical computer science problems.

Supervised Learning

Five classification algorithms (decision trees, boosting, k-nearest neighbors, support vector machines and neural networks) were compared and contrasted on two different datasets.