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Grades: 541
Median GPA: A-
Mean GPA: 3.450
3.6
Professor rating
4.2
Difficulty
16
Ratings given
71%
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
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Grades: 541
Median GPA: A-
Mean GPA: 3.450
3.6
Professor rating
4.2
Difficulty
16
Ratings given
71%
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
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