SMA 327 - Linear Algebra II (Mathematics)

3 Credit(s)

This course explores computational linear algebra and its applications in data analysis. Students learn matrix norms, conditioning, LU/QR and other factorizations, SVD, PCA, and iterative eigenvalue algorithms. Emphasis is placed on numerical stability and interpreting results in real contexts, to develop skills in implementing algorithms, analyzing datasets, and building complete computational workflows. Software required (see instructor).

When Offered: As needed.
Prerequisite(s): SCS 132 and SMA 225.