Physical Sciences and Mathematics Commons^™

A Machine Learning Approach To Sector Based Market Efficiency, Angus Zuklie

Honors Projects

In economic circles, there is an idea that the increasing prevalence of algorithmic trading is improving the information efficiency of electronic stock markets. This project sought to test the above theory computationally. If an algorithm can accurately forecast near-term equity prices using historical data, there must be predictive information present in the data. Changes in the predictive accuracy of such algorithms should correlate with increasing or decreasing market efficiency.

By using advanced machine learning approaches, including dense neural networks, LSTM, and CNN models, I modified intra day predictive precision to act as a proxy for market efficiency. Allowing for the …

Exploiting Context In Linear Influence Games: Improved Algorithms For Model Selection And Performance Evaluation, Daniel Little

Honors Projects

In the recent past, extensive experimental works have been performed to predict joint voting outcomes in Congress based on a game-theoretic model of voting behavior known as Linear Influence Games. In this thesis, we improve the model selection and evaluation procedure of these past experiments. First, we implement two methods, Nested Cross-Validation with Tuning (Nested CVT) and Bootstrap Bias Corrected Cross-Validation (BBC-CV), to perform model selection and evaluation with less bias than previous methods. While Nested CVT is a commonly used method, it requires learning a large number of models; BBC-CV is a more recent method boasting less computational cost. …

Using Machine Learning For Detection Of Covid-19, Justin Rickert

Honors Projects

Currently, the most widely used diagnostic tool for COVID-19 is the RT-PCR nasal swab test recommended by the CDC. However, some studies have shown that chest CT scans have the potential to be more accurate and are also capable of detecting the virus in its earlier stages. Unfortunately, CT results are not instantaneously available as it may be days before a radiologist can review the scan. This delay is one of the factors preventing the widespread use of CT scans for COVID detection. To address the delay, this project investigated Convolutional Neural Networks, an advanced form of machine learning used …

Teaching Computers To Teach Themselves: Synthesizing Training Data Based On Human-Perceived Elements, James Little

Honors Projects

Isolation-Based Scene Generation (IBSG) is a process for creating synthetic datasets made to train machine learning detectors and classifiers. In this project, we formalize the IBSG process and describe the scenarios—object detection and object classification given audio or image input—in which it can be useful. We then look at the Stanford Street View House Number (SVHN) dataset and build several different IBSG training datasets based on existing SVHN data. We try to improve the compositing algorithm used to build the IBSG dataset so that models trained with synthetic data perform as well as models trained with the original SVHN training …