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5.0
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5.0
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1,000
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99%
Would take again
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| Name | Grades | Rating | |||
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CGS 4315 Richard Golden | |||||
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CGS 4315 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
Intelligent Systems Design
CGS 4315 (Same as CS 4315)
School of Behavioral and Brain Sciences
This advanced machine learning course covers mathematics essential for the analysis and design of unsupervised, supervised, and reinforcement machine learning algorithms including deep learning neural network models formulated within a statistical empirical risk minimization framework. Topics include: convergence analysis of adaptive and batch learning algorithms, Monte Carlo Markov Chain inference algorithms, bootstrap sampling methods, and the statistical analysis of generalization performance using model selection measures such as AIC and BIC. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisite: CGS 4314 or CS 4314.
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
Intelligent Systems Design
CGS 4315 (Same as CS 4315)
School of Behavioral and Brain Sciences
This advanced machine learning course covers mathematics essential for the analysis and design of unsupervised, supervised, and reinforcement machine learning algorithms including deep learning neural network models formulated within a statistical empirical risk minimization framework. Topics include: convergence analysis of adaptive and batch learning algorithms, Monte Carlo Markov Chain inference algorithms, bootstrap sampling methods, and the statistical analysis of generalization performance using model selection measures such as AIC and BIC. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisite: CGS 4314 or CS 4314.
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.