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Jan 28, 2025
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MATH 215 - Computational Matrix Algebra and Applications
Semester Hours: 3
Computational methods for matrix analysis are introduced; these include LU decomposition, Cholesky decomposition, singular value decomposition, and QR decomposition. Various algorithms for these decompositions are covered in detail and implemented using MATLAB. Applications of
these methods, including dimension reduction, principal component analysis, and eigenvalue problems, are discussed.
Prerequisite(s)/Course Notes:
Course open to graduate students in Computer Science/Data Science, others need permission from computer science graduate director. The first course in Linear Algebra (Math 135A) is recommended.
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