Machine learning algorithms, how they work, and their uses.
| dc.contributor.author | Messier, Seth | |
| dc.date.accessioned | 2022-09-08T16:45:28Z | |
| dc.date.available | 2022-09-08T16:45:28Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-09-08 | |
| dc.description.abstract | Machine learning is a methodology that enables computers to predict outcomes based on specific input data. It is a broad field that is used to help identify trends in data and allow everything from personalized recommendations for things like movies and ads to self-driving cars. While there are many different machine learning algorithms, four of the most commonly used ones include, linear regression, logistic regression, neural networks, and collaborative filtering. Each of these algorithms has its different strengths, weaknesses, and primary uses. Machine learning algorithms have created a large number of useful tools. The application of these different tools has has and can be used across a wide variety of fields ranging from healthcare to climate change. My summer research project involved understanding the underlying algorithms used in machine learning and applying them to small-scale projects to gain a deeper understanding of how machines learn. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Undergraduate | en_US |
| dc.description.sponsorship | Valerie Kuehne Undergraduate Research Awards (VKURA) | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14228 | |
| dc.language.iso | en | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | linear regression | en_US |
| dc.subject | logistic regression | en_US |
| dc.subject | neural networks | en_US |
| dc.subject | collaborative filtering | en_US |
| dc.title | Machine learning algorithms, how they work, and their uses. | en_US |
| dc.type | Poster | en_US |