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5.0
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5.0
Difficulty
1,000
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99%
Would take again
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Search Results
| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 6375 William Semper | |||||
A | |||||
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 6375 William Semper | |||||
A | |||||
William Semper
[email protected]Grades: 261
Median GPA: A
Mean GPA: 3.700
4.7
Professor rating
2.6
Difficulty
21
Ratings given
100%
Would take again
Machine Learning
CS 6375
Erik Jonsson School of Engineering and Computer Science
Algorithms for training perceptions and multi-layer neural nets: back propagation, Boltzmann machines, and self-organizing nets. The ID3 and the Nearest Neighbor algorithms. Formal models for analyzing learnability: exact identification in the limit and probably approximately correct (PAC) identification. Computational limitations of learning machines. 3 credit hours.
Prerequisite: CS 5343.
Offering Frequency: Each year
Grades: 3,824
Median GPA: A-
Mean GPA: 3.639
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William Semper
[email protected]Grades: 261
Median GPA: A
Mean GPA: 3.700
4.7
Professor rating
2.6
Difficulty
21
Ratings given
100%
Would take again
Machine Learning
CS 6375
Erik Jonsson School of Engineering and Computer Science
Algorithms for training perceptions and multi-layer neural nets: back propagation, Boltzmann machines, and self-organizing nets. The ID3 and the Nearest Neighbor algorithms. Formal models for analyzing learnability: exact identification in the limit and probably approximately correct (PAC) identification. Computational limitations of learning machines. 3 credit hours.
Prerequisite: CS 5343.
Offering Frequency: Each year
Grades: 3,824
Median GPA: A-
Mean GPA: 3.639
Click a checkbox to add something to compare.