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MATH 662: 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


Basic mathematical and probabilistic models and derivations for linear and logistic regression including regularization and application to SVM and PCA. Mathematical and numerical aspects of classical learning methods such as Kernel methods andgradient based methods including neural networks. Incorporates modern tools such as Python, shell tools, and version control. Includes industrial scale applications in satellite imagery, physics, biology and engineering. Computational and analytic assignments are given. Offered by Mathematics. Limited to three attempts.
Recommended Prerequisite: CS 112 or equivalent.
Registration Restrictions:

Enrollment limited to students with a class of Advanced to Candidacy, Graduate, Junior Plus, Non-Degree or Senior Plus.

Students in a Non-Degree Undergraduate degree may not enroll.

Schedule Type: Lecture
This course is graded on the Graduate Regular scale.