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Machine Learning and Control Theory

MATH 6335

School of Natural Sciences and Mathematics

Course covers modern methods of control theory applied to machine learning and machine learning methods applied to the control and identification of dynamical systems. Topics include supervised learning and gradient methods, stochastic gradient, optimal control theory, dynamic programming, applications to deep learning, Markov decision processes, reinforcement learning, and identification and control of systems via gradient techniques. 3 credit hours.

Prerequisite: MATH 4355 or equivalent or instructor consent required.

Offering Frequency: Each year

Grades: 26

Median GPA: B+

Mean GPA: 3.448

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