MATH 302 Matrix Methods in Data Analysis and Machine Learning
This course is designed to provide topics in linear algebra and related mathematics for students to gain a firm understanding of data science and machine learning through lecture, discussion, problem-solving and projects. Topics include matrix algebra concepts such as positive definite matrices, singular values and singular vectors in the singular value decomposition, and principal components; computation with large matrices with a focus on matrix factorization, iterative methods, low-rank and sparse approximation techniques; special matrices; optimization techniques; neural nets; and the learning function.
Cross Listed Courses
MATH 302 &MATH 502