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Intelligent Systems Analysis
ACN 6347
School of Behavioral and Brain Sciences
Mathematical tools for investigating the asymptotic behavior of both deterministic and stochastic nonlinear optimization methods for machine learning algorithms. Topics include: artificial neural network architectures, Lyapunov stability theory, nonlinear optimization theory, stochastic approximation theory, and Monte Carlo Markov Chain methods such as the Metropolis-Hastings algorithm. Emphasizes development of advanced analytic skills and mathematical reasoning abilities. 3 credit hours.
Prerequisites: (Linear algebra, multivariable calculus, and STAT 3341 or equivalent) and BBSC majors only and department consent required.
Offering Frequency: Every two years
Grades: 15
Median GPA: A
Mean GPA: 3.712
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Intelligent Systems Analysis
ACN 6347
School of Behavioral and Brain Sciences
Mathematical tools for investigating the asymptotic behavior of both deterministic and stochastic nonlinear optimization methods for machine learning algorithms. Topics include: artificial neural network architectures, Lyapunov stability theory, nonlinear optimization theory, stochastic approximation theory, and Monte Carlo Markov Chain methods such as the Metropolis-Hastings algorithm. Emphasizes development of advanced analytic skills and mathematical reasoning abilities. 3 credit hours.
Prerequisites: (Linear algebra, multivariable calculus, and STAT 3341 or equivalent) and BBSC majors only and department consent required.
Offering Frequency: Every two years
Grades: 15
Median GPA: A
Mean GPA: 3.712
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