An introduction to the mathematics of matrices and their application in traditional and modern methods of data analysis and scientific computing. The course promotes an interdisciplinary approach, and topics may include:



- Coordinate transformations in data visualization, iterated patterns in mosaics and art

- Use of least squares approach with linear solvers for regression, as well as image alignment

- PCA-decomposition of computer images (the "eigenfaces" example)

- Matrix decomposition methods: Singular Value Decomposition and Non-negative Matrix Factorization

- Dimensionality reduction and data compression

- Matrix operations in deep learning.

- ChatGPT and word vector embeddings



Students will use R for most of the computational tasks, and Mathematica for symbolic math and deep learning.
Offering: Host
Grading: A-F
Credits: 1.00
Gen Ed Area: SBS-QAC
Prereq: MATH121