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Grades: 846
Median GPA: B+
Mean GPA: 3.012
2.1
Professor rating
4.1
Difficulty
23
Ratings given
25%
Would take again
Machine Learning and Pattern Recognition
EEGR 6364
Erik Jonsson School of Engineering and Computer Science
This course covers basic concepts and algorithms for pattern recognition and machine learning. Bayesian decision theory, parametric learning, non-parametric learning, linear regression, linear classifiers and support vector machine, kernel methods, data clustering, mixture models, component analysis, multilayer neural networks, and deep learning with convolutional neural networks. 3 credit hours.
Prerequisites: Knowledge of probability and knowledge of MATLAB or C.
Offering Frequency: Every two years
Click a checkbox to add something to compare.
Grades: 846
Median GPA: B+
Mean GPA: 3.012
2.1
Professor rating
4.1
Difficulty
23
Ratings given
25%
Would take again
Machine Learning and Pattern Recognition
EEGR 6364
Erik Jonsson School of Engineering and Computer Science
This course covers basic concepts and algorithms for pattern recognition and machine learning. Bayesian decision theory, parametric learning, non-parametric learning, linear regression, linear classifiers and support vector machine, kernel methods, data clustering, mixture models, component analysis, multilayer neural networks, and deep learning with convolutional neural networks. 3 credit hours.
Prerequisites: Knowledge of probability and knowledge of MATLAB or C.
Offering Frequency: Every two years
Click a checkbox to add something to compare.
