dc.contributor.author | Medina, Francis Patricia | |
dc.date.accessioned | 2021-08-02T20:25:16Z | |
dc.date.available | 2021-08-02T20:25:16Z | |
dc.date.issued | 2020-09 | |
dc.identifier.citation | Medina, Francis Patricia. COM 3910: Mathematical Foundations of Machine Learning, crn13760, Yeshiva College. | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12202/7005 | |
dc.description | Course syllabus / YU only | en_US |
dc.description.abstract | Data science primarily focuses on identifying patterns and information in existing data, and predicting future data values. To become a highly skilled data scientist one must understand, and know how to properly apply, probability and statistics. Computer scientists, even those who do not study or make use of machine learning or other aspects of C.S. that make heavy use of probabilistic approaches, must have a core understanding of the mathematics behind data science since they are often called upon to take the work of a data scientist and adapt it to run reliably and consistently at a large scale.
In order to fully understand Machine Learning, one must have a thorough grounding in calculus, linear algebra, probability and statistics, as well as the ability to program solutions to statistical problems. This course provides the aforementioned foundations. | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartofseries | Yeshiva College Syllabi;COM3910 | |
dc.subject | course syllabus | en_US |
dc.subject | mathematics | en_US |
dc.subject | machine learning | en_US |
dc.subject | computer science | en_US |
dc.title | COM 3910: Mathematical Foundations of Machine Learning | en_US |
dc.type | Learning Object | en_US |