MATH 465: Mathematics of Data Science
Important: For the most up-to-date information, refer to the official George Mason Course Catalog
General Information
Credits: 3
Description:
Covers mathematical aspects of data science including theory of linear and nonlinear dimension reduction, elements of spectral graph theory, function spaces and regularity in regression, and data-driven dynamics identification and discovery. Computational and analytic assignments are given. Offered by Mathematics. Limited to three attempts.
Recommended Prerequisite: MATH 352 or STAT 350 or STAT 360 or STAT 356
Registration Restrictions:
Required Prerequisites: ((MATH 214C or 214XS) and (MATH 464C or 464XS)).
C Requires minimum grade of C.
XS Requires minimum grade of XS.
Schedule Type: Lecture
Grading:
This course is graded on the Undergraduate Regular scale.
This course is graded on the Undergraduate Regular scale.