Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Computer algorithms help in gaining experience to improve the facility over time for use by both consumers and corporations. In today’s technologically advanced world, Machine Learning has given us self-driving cars, speech recognition software, and AI agents like Siri and Google assistant. This project evaluates how the Beta function came to be and how Stirling’s formula is implemented in calculating the magnitude of this function for large input values. The Beta function can then be used to produce a Beta distribution of probabilities to find whether people will actually watch a video they come across on their recommendations feed or search feed and then using Bayesian inference update the prior set predictions.
"Probabilistic Machine Learning Using Bayesian Inference,"
Undergraduate Journal of Mathematical Modeling: One + Two:
1, Article 1.
DOI: https://doi.org/10.5038/2326-36220.127.116.1120 Available at: https://digitalcommons.usf.edu/ujmm/vol11/iss1/1
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Arcadii Grinshpan, Mathematics and Statistics
James Anderson, Computer Science and Engineering
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