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5.0
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5.0
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1,000
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99%
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Search Results
| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CS 6344 Haim Schweitzer | |||||
A- | |||||
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| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
CS 6344 Haim Schweitzer | |||||
A- | |||||
Grades: 1,700
Median GPA: A-
Mean GPA: 3.495
2.9
Professor rating
2.4
Difficulty
37
Ratings given
31%
Would take again
Data Representations
CS 6344
Erik Jonsson School of Engineering and Computer Science
Useful representations of data for efficient manipulation and visualization. These include, among others Dimensionality Reduction, Clustering, Euclidean Embedding, Graph Embedding, and Discriminant Functions. Techniques covered include principal component analysis (PCA), singular value decomposition (SVD), clustering, and various randomized techniques. Special emphasis is given to the performance of these techniques on big data. 3 credit hours.
Prerequisite: CS 5343.
Offering Frequency: Each year
Grades: 309
Median GPA: A-
Mean GPA: 3.478
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Grades: 1,700
Median GPA: A-
Mean GPA: 3.495
2.9
Professor rating
2.4
Difficulty
37
Ratings given
31%
Would take again
Data Representations
CS 6344
Erik Jonsson School of Engineering and Computer Science
Useful representations of data for efficient manipulation and visualization. These include, among others Dimensionality Reduction, Clustering, Euclidean Embedding, Graph Embedding, and Discriminant Functions. Techniques covered include principal component analysis (PCA), singular value decomposition (SVD), clustering, and various randomized techniques. Special emphasis is given to the performance of these techniques on big data. 3 credit hours.
Prerequisite: CS 5343.
Offering Frequency: Each year
Grades: 309
Median GPA: A-
Mean GPA: 3.478
Click a checkbox to add something to compare.