MATH 462: Mathematics of Machine Learning and Industrial Applications I

Important: For the most up-to-date information, refer to the official George Mason Course Catalog

General Information

Credits: 2

Description:

Basic mathematical optimization and probability theory in the context of classical learning and gradient based methods including neural networks. Incorporates modern tools such as Python, shell tools, and version control. Includes industrial applications in satellite imagery, physics, biology and engineering. Computational and analytic assignments are given. Offered by Mathematics. Limited to three attempts.

Registration Restrictions:

Required Prerequisites: ((MATH 203^C or 203^XS) and MATH 213^C or 213^XS) and (CS 112^C or 112^XS).
^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.