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Intelligent Systems Analysis
CS 4314 (Same as CGS 4314)
Erik Jonsson School of Engineering and Computer Science
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: 145
Median GPA: B
Mean GPA: 3.030
Click a checkbox to add something to compare.
Intelligent Systems Analysis
CS 4314 (Same as CGS 4314)
Erik Jonsson School of Engineering and Computer Science
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: 145
Median GPA: B
Mean GPA: 3.030
Click a checkbox to add something to compare.