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5.0
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5.0
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1,000
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99%
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Not teaching in Spring 2026 | |||||
HCS 6349 Richard Golden | |||||
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Not teaching in Spring 2026 | |||||
HCS 6349 Richard Golden | |||||
Grades: 546
Median GPA: A-
Mean GPA: 3.463
3.7
Professor rating
4.2
Difficulty
17
Ratings given
73%
Would take again
Statistical Machine Learning
HCS 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
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Grades: 546
Median GPA: A-
Mean GPA: 3.463
3.7
Professor rating
4.2
Difficulty
17
Ratings given
73%
Would take again
Statistical Machine Learning
HCS 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
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.