Texas A&M University 3 credits MATH
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; enrolled in MS in Data Science, MS in Quantitative Finance, or MS in Mathematics.
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