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dc.contributor.authorGlassman, Zachary
dc.date.accessioned2021-10-12T13:12:36Z
dc.date.available2021-10-12T13:12:36Z
dc.date.issued2021-09
dc.identifier.citationGlassman, Zachary. (2021, Fall), Syllabus, COMP3920F: Applied Machine Learning, Stern College for Women, Yeshiva University.
dc.identifier.urihttps://hdl.handle.net/20.500.12202/7315
dc.descriptionSCW syllabus / YU onlyen_US
dc.description.abstractOverview: This course provides an introduction to the practice and theory of machine learning. Upon successful completion of this course you will understand how to apply machine learning to real world problems through proper model and model metric selection. You will also gain experience implementing a real machine learning project leveraging what you have learned in the course. This course covers a wide variety of machine learning topics balancing between theory of machine learning and practical applied skills. The course will involve writing Python code both for labs, home, and exams, however, students are expected to be competent in Python programming through prior programming or independent study. Additionally, students are expected to have a good grasp ofen_US
dc.language.isoen_USen_US
dc.subjectMachine Learningen_US
dc.titleCOMP3920F: Applied Machine Learning - Fall 2021en_US
dc.typeLearning Objecten_US


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