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5.0
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Search Results
| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
MATH 6322 Viswanath Ramakrishna | |||||
A | |||||
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| Name | Grades | Rating | |||
|---|---|---|---|---|---|
Not teaching in Spring 2026 | |||||
MATH 6322 Viswanath Ramakrishna | |||||
A | |||||
Grades: 1,249
Median GPA: A
Mean GPA: 3.380
4
Professor rating
3.2
Difficulty
60
Ratings given
76%
Would take again
Mathematical Foundations of Data Science
MATH 6322
School of Natural Sciences and Mathematics
Mathematics of data science and machine learning, clustering algorithms, principal component analysis, perceptrons and support vector machines, convex optimization, ordinary and stochastic gradient descent, kernel based learning, large deviation inequalities and PAC learning, feedforward neural networks, and backpropagation. 3 credit hours.
Prerequisites: (MATH 2418 or equivalent and MATH 2415 or equivalent) and instructor consent required.
Offering Frequency: Each year
Grades: 142
Median GPA: A
Mean GPA: 3.684
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Grades: 1,249
Median GPA: A
Mean GPA: 3.380
4
Professor rating
3.2
Difficulty
60
Ratings given
76%
Would take again
Mathematical Foundations of Data Science
MATH 6322
School of Natural Sciences and Mathematics
Mathematics of data science and machine learning, clustering algorithms, principal component analysis, perceptrons and support vector machines, convex optimization, ordinary and stochastic gradient descent, kernel based learning, large deviation inequalities and PAC learning, feedforward neural networks, and backpropagation. 3 credit hours.
Prerequisites: (MATH 2418 or equivalent and MATH 2415 or equivalent) and instructor consent required.
Offering Frequency: Each year
Grades: 142
Median GPA: A
Mean GPA: 3.684
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