MenuMATH 677 - Mathematical Foundations for Data Science - Fall 2024
Credits 3. 3 Lecture Hours.
Linear systems; least squares problems; eigenvalue decomposition; singular value decomposition; Perron–Frobenius theory; dynamic programming; convex optimization; gradient descent; linear programming; semidefinite programming; compressive sensing.
Prerequisites: MATH 304, MATH 309, MATH 311, MATH 323, or equivalent; admission to master of science in data science or master of science in quantitative finance.
Sections
| Sec | Instructor | Lecture | |
|---|---|---|---|
| 600 | Thicke,Kyle S | T R 08:00-09:15 BLOC 117 |
|
| 601 | Thicke,Kyle S | T R 12:45-14:00 BLOC 128 |
