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Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
CS 6375 (Overall) | |||||
A- | |||||
CS 6375 Nicholas Ruozzi | |||||
B+ | |||||
CS 6375 Wei Yang | |||||
A- | |||||
Not teaching in Spring 2026 | |||||
CS 6375 Anjum Chida | |||||
A | |||||
CS 6375 Partha De | |||||
A | |||||
CS 6375 Vibhav Gogate | |||||
B+ | |||||
CS 6375 Rishabh Iyer | |||||
A- | |||||
CS 6375 Gautam Kunapuli | |||||
A- | |||||
CS 6375 Crystal Maung | |||||
A | |||||
CS 6375 Anurag Nagar | |||||
A- | |||||
CS 6375 Sriraam Natarajan | |||||
A- | |||||
CS 6375 Tahrima Rahman | |||||
CS 6375 Haim Schweitzer | |||||
A- | |||||
CS 6375 William Semper | |||||
A | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
CS 6375 (Overall) | |||||
A- | |||||
CS 6375 Nicholas Ruozzi | |||||
B+ | |||||
CS 6375 Wei Yang | |||||
A- | |||||
Not teaching in Spring 2026 | |||||
CS 6375 Anjum Chida | |||||
A | |||||
CS 6375 Partha De | |||||
A | |||||
CS 6375 Vibhav Gogate | |||||
B+ | |||||
CS 6375 Rishabh Iyer | |||||
A- | |||||
CS 6375 Gautam Kunapuli | |||||
A- | |||||
CS 6375 Crystal Maung | |||||
A | |||||
CS 6375 Anurag Nagar | |||||
A- | |||||
CS 6375 Sriraam Natarajan | |||||
A- | |||||
CS 6375 Tahrima Rahman | |||||
CS 6375 Haim Schweitzer | |||||
A- | |||||
CS 6375 William Semper | |||||
A | |||||
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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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.