Results - ACN 6349 - UTD TRENDS

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Statistical Machine Learning

ACN 6349

School of Behavioral and Brain Sciences

Mathematical tools for investigating the asymptotic behavior of both batch and adaptive machine learning algorithms including convergence of gradient descent batch learning algorithms convergence of adaptive stochastic approximation learning algorithms, and convergence of Monte Carlo Markov Chain algorithms. M-estimation and bootstrap asymptotic statistical theory for characterizing asymptotic behavior of parameter estimates as a function of sample size to support model selection, specification analysis, and hypothesis testing. Emphasizes applications of theory to unsupervised, supervised, and reinforcement learning machines and deep learning with MATLAB software implementations. 3 credit hours.

Prerequisites: (ACN 6348 or HCS 6348) and department consent required.

Offering Frequency: Every two years

Grades: 30

Median GPA: A

Mean GPA: 3.845

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