Category: Computer Science
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Reinforcement Learning: Does Memory Improve an AI’s Performance?
By Olivia Buchanan Faculty Mentor: Evan Coleman Abstract Reinforcement learning (RL) enables AI agents to solve complex problems through trial and error instead of using human-coded instructions. This project investigates how these agents perform when information about the world is hidden from them, a scenario that mimics real-life challenges such as a broken sensor. Specifically,…
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Predicting Sports Injuries Using Machine Learning
By Madison McCarty Faculty Mentor: Evan Coleman Abstract This project investigates the use of machine learning to predict injury risk across two distinct datasets characterized by extreme class imbalance. Injuries represented approximately 1–2% of all observations, making traditional accuracy-based evaluation insufficient. To address this challenge, data balancing techniques and cost‑sensitive learning were applied to emphasize…
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Sports, Sentiment, and Stock Returns: A Student Research Project in Business Analytics
By Cooper Chasse Faculty Mentor: Dr. Jeff Solka Abstract This project presents a machine learning pipeline for predicting daily stock returns of sports-sector companies, using Nike (NKE) as the primary case study. The pipeline combines traditional price-based technical features with four natural language processing (NLP) sentiment signals: general news sentiment sourced from the GDELT news…
