MATH 216A. Mathematical Methods in Data Science I (4 units)
Link to catalog page: https://catalog.ucsd.edu/courses/MATH.html#math216a
Description
This is the first course in a three-course sequence in mathematical methods in data science, and will serve as an introduction to the rest of the sequence. Topics include principal component analysis and the singular value decomposition, sparse representation, dictionary learning, the Johnson Lindenstrauss Lemma and its applications, compressed sensing, kernel methods, nearest neighbor searches, and spectral and subspace clustering. Some scientific programming experience is recommended. Prerequisites: graduate standing.
Prerequisite courses
MATH 216A has no prerequisite courses.
Successor courses
MATH 216A is a prerequisite of the following 1 courses: