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Search Results
| Name | Grades | Rating | |||
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MATH 6322 (Overall) | |||||
A | |||||
MATH 6322 Viswanath Ramakrishna | |||||
A | |||||
MATH 6322 Nan Wu | |||||
A | |||||
Search Results
| Name | Grades | Rating | |||
|---|---|---|---|---|---|
MATH 6322 (Overall) | |||||
A | |||||
MATH 6322 Viswanath Ramakrishna | |||||
A | |||||
MATH 6322 Nan Wu | |||||
A | |||||
Mathematical Foundations of Data Science
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: 127
Median GPA: A
Mean GPA: 3.662
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Mathematical Foundations of Data Science
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: 127
Median GPA: A
Mean GPA: 3.662
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