Show simple item record

dc.contributor.authorMedina, Francis Patricia
dc.date.accessioned2021-08-02T20:25:16Z
dc.date.available2021-08-02T20:25:16Z
dc.date.issued2020-09
dc.identifier.citationMedina, Francis Patricia. COM 3910: Mathematical Foundations of Machine Learning, crn13760, Yeshiva College.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12202/7005
dc.descriptionCourse syllabus / YU onlyen_US
dc.description.abstractData 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.isoen_USen_US
dc.relation.ispartofseriesYeshiva College Syllabi;COM3910
dc.subjectcourse syllabusen_US
dc.subjectmathematicsen_US
dc.subjectmachine learningen_US
dc.subjectcomputer scienceen_US
dc.titleCOM 3910: Mathematical Foundations of Machine Learningen_US
dc.typeLearning Objecten_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record