Date of Award
Bachelor of Science (B.S.) in Computer Engineering and University Honors
Machine learning -- History, Machine learning -- Technological innovations, Open source software -- Evaluation, Computer security, Bayesian statistical decision theory, Neural networks (Computer science)
This paper examines the history and current state of machine learning. It examines neural networks, theory behind neural networks, how they are implemented, and how they are used. The systems and networks examined have up to three modes of learning. Theory behind machine learning is broken up into three approaches; rule-based, Bayesian, and neural networks. Operation of machine learning algorithms has been enabled by several prevalent libraries in the open source community, as well as various hardware technologies. Due to this surge in resources application developers have been able to apply machine learning in novel ways. An application of machine learning to evaluate the security practices of open source software was undertaken as the culmination of this thesis.
Andersen, John, "Theory, Operation, and Application of Neural Networks" (2018). University Honors Theses. Paper 535.