MATH344
Optimization and Machine Learning Mathematics
Mathematics & Computer Science
College of Liberal Arts
Course Subject Code
MATH
Course Number
344
Status
Active
Course Attributes
EMMT: Major-Mathematics Elective, MMAT: Major-Mathematics, ULVL: Upper Level UG Course, ENMH: Minor-Mathematics Elective, NMAT: Minor-Mathematics
Course Short Title
Optimization
Course Long Title
Optimization and Machine Learning Mathematics
Course Description
Presents the mathematics behind how and why optimization algorithms work; finding maximum and minimum values of functions with and without constraints; convexity and concavity, the Hessian matrix, Newton-Raphson Method, classical unconstrained optimization (gradient methods, Newton’s Method), classical constrained optimization (Lagrange Multipliers, Kuhn-Tucker Theory), and linear algebra topics (orthogonality, orthogonal projections, Gram-Schmidt orthogonalization, QR factorization, least squares and weighted least squares, symmetric matrices and quadratic forms, singular value decomposition (SVD) and principal component analysis (PCA)).
Min
4
Repeatable
-
Course Restrictions
Level: UG (I),
Equivalent Course(s)
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