MTH 348
Computational Linear Algebra · 4 credits · Spring 2027
Requirements
Pre-requisite MTH 338
About this course
Builds on the material in MTH 338 Linear Algebra with an emphasis on effective computational approaches as a foundation for further study in data science, machine learning and AI. Topics include matrix canonical forms, exponentials, tensor products, singular value decompositions, principal component analysis and compressed sensing. Computational aspects include stability, speed, resource usage, effective factorization methods, stochastic gradient descent and L1 methods for sparse matrices.
Seat status as of Oct 2, 10:27 AM ET. Seats can change between refreshes.
No sections have been posted for this course in this term yet.