Machine Learning Projects & Reports
These academic projects and reports were completed during my MS in Computer Science at Georgia Tech, with a Machine Learning and Computing Systems focus. I completed the degree in December 2024 with a 3.90 GPA while working full-time in engineering leadership. The reports below share the methods, experiments, and findings from that graduate coursework. Please note some parts have been redacted to remove personally identifiable information.
Supervised Learning
Compared classifiers on the UCI Adult Census and Spambase datasets using scikit-learn. The experiments covered preprocessing, stratified validation, hyperparameter search, learning curves, weighted F1, and ROC/AUC analysis.
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Unsupervised Learning
Studied clustering and dimensionality reduction on the same datasets, comparing cluster quality, reconstruction loss, and the effects on downstream classification.
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Randomized Optimization
Used Java/ABAGAIL and Python to compare optimization approaches and neural-network weight search. Analyzed experiment results with pandas and matplotlib.
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Reinforcement Learning
Wrote custom value-iteration, policy-iteration, and Q-learning solvers for a grid-world problem. Compared sequential decision-making approaches and used an MDP toolbox for an additional environment.
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