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.