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.
Above information is from 202331 term.
Sections
| Sec | Instructor | Lecture |
|---|---|---|
| 699 | Kuchment,Peter | N/A-N/A ONLINE |
| 700 | Kuchment,Peter | N/A-N/A ONLINE |