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5.0
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5.0
Difficulty
1,000
Ratings given
99%
Would take again
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GEOS 5338 David Lary | |||||
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GEOS 5338 David Lary | |||||
Grades: 183
Median GPA: A
Mean GPA: 3.952
3.1
Professor rating
1.6
Difficulty
17
Ratings given
100%
Would take again
Big Data and Machine Learning for Scientific Discovery
GEOS 5338
School of Natural Sciences and Mathematics
This class introduces a wide range of machine learning techniques suitable for Big Data analysis. The techniques covered include multivariate non-linear non-parametric regression and classification, both supervised and unsupervised. These approaches are directly applicable to many issues of major scientific and societal importance. The practical tools introduced (Neural Networks, Support Vector Regression, Decision Trees, Random Forests, etc) can be readily used in a wide range of applications from research to real time decision support. The data used can come from a wide variety of sources including scientific instrumentation, social media, remote sensing, aerial vehicles, and the internet of things. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Grades: 183
Median GPA: A
Mean GPA: 3.952
3.1
Professor rating
1.6
Difficulty
17
Ratings given
100%
Would take again
Big Data and Machine Learning for Scientific Discovery
GEOS 5338
School of Natural Sciences and Mathematics
This class introduces a wide range of machine learning techniques suitable for Big Data analysis. The techniques covered include multivariate non-linear non-parametric regression and classification, both supervised and unsupervised. These approaches are directly applicable to many issues of major scientific and societal importance. The practical tools introduced (Neural Networks, Support Vector Regression, Decision Trees, Random Forests, etc) can be readily used in a wide range of applications from research to real time decision support. The data used can come from a wide variety of sources including scientific instrumentation, social media, remote sensing, aerial vehicles, and the internet of things. 3 credit hours.
Offering Frequency: Based on student interest and instructor availability
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.