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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 Analysis
CGS 4314 (Same as CS 4314)
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. Course topics include: advanced vector and matrix calculus and stochastic sequences of mixed random vectors, Markov fields, and Bayesian nets. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisites: (MATH 2414 or MATH 2419) and (CS 3341 or SE 3341) and MATH 2418 or instructor consent required.
Offering Frequency: Every two years
Grades: 37
Median GPA: B
Mean GPA: 2.960
Click a checkbox to add something to compare.
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 Analysis
CGS 4314 (Same as CS 4314)
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. Course topics include: advanced vector and matrix calculus and stochastic sequences of mixed random vectors, Markov fields, and Bayesian nets. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. 3 credit hours.
Prerequisites: (MATH 2414 or MATH 2419) and (CS 3341 or SE 3341) and MATH 2418 or instructor consent required.
Offering Frequency: Every two years
Grades: 37
Median GPA: B
Mean GPA: 2.960
Click a checkbox to add something to compare.
