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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
MATH 6335 Alain Bensoussan | |||||
B+ | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
MATH 6335 Alain Bensoussan | |||||
B+ | |||||
Grades: 98
Median GPA: A-
Mean GPA: 3.525
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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Grades: 98
Median GPA: A-
Mean GPA: 3.525
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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