MATH 667 - Machine Learning

Units: 4
Introduction to machine learning with an emphasis on the underlying mathematics. Topics such as VC dimension, central limit theorem, gradient descent. Applications such as SVM and neural nets.

Prerequisites: MATH 230 and MATH 310; or M.S. Computer Science major.
Possible Instructional Methods: Entirely On-ground.
Grading: A-F grading only.
Cross-listed: CS 667.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply the fundamental definitions and theorems of data analysis.
Apply techniques and algorithms of data analysis.