MATH 247
Fall 2024
| Section:
01
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This is a course on topics in linear algebra that are the foundations of modern data science and machine learning. Topics include dimensionality reductions, matrix decompositions, elementary spectral theory, multivariate optimizations, and their applications to machine learning such as gradient descent and support vector machines. |
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| Credit: 1 |
Gen Ed Area Dept:
NSM MATH |
| Course Format: Lecture | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: (MATH221 OR MATH223) AND MATH222 |
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Fulfills a Major Requirement for: (MATH) |
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Past Enrollment Probability: 75% - 89% |
| SECTION 01 |
| Instructor(s): Yoon,Iris Times: .M.W... 02:50PM-04:10PM; Location: TBA |
| Total Enrollment Limit: 29 | | SR major: 10 | JR major: 10 | | |
| Seats Available: 0 | GRAD: X | SR non-major: 2 | JR non-major: 2 | SO: 5 | FR: 0 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 9 | 1st Ranked: 5 | 2nd Ranked: 0 | 3rd Ranked: 4 | 4th Ranked: 0 | Unranked: 0 |
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