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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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GISC 6323 Michael Tiefelsdorf | |||||
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GISC 6323 Michael Tiefelsdorf | |||||
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Grades: 368
Median GPA: B+
Mean GPA: 3.317
2.2
Professor rating
4.2
Difficulty
10
Ratings given
17%
Would take again
Machine Learning for Socio-Economic and Geo-Referenced Data
GISC 6323
School of Economic, Political and Policy Sciences
Models and algorithms as well as their underlying conceptional foundations to structure dynamic socio-economic and geo-referenced data are introduced. Open-source software and commonly available hardware are used. Practical examples of [a] supervised machine learning to develop classification rules and [b] unsupervised data mining to uncover a hidden organization of data objects are used to explore the strength and weaknesses of selected data analytical methods and to examine the resulting output. Where appropriate, ethical ramifications are discussed. 3 credit hours.
Offering Frequency: Spring
Grades: 22
Median GPA: A-
Mean GPA: 3.395
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Grades: 368
Median GPA: B+
Mean GPA: 3.317
2.2
Professor rating
4.2
Difficulty
10
Ratings given
17%
Would take again
Machine Learning for Socio-Economic and Geo-Referenced Data
GISC 6323
School of Economic, Political and Policy Sciences
Models and algorithms as well as their underlying conceptional foundations to structure dynamic socio-economic and geo-referenced data are introduced. Open-source software and commonly available hardware are used. Practical examples of [a] supervised machine learning to develop classification rules and [b] unsupervised data mining to uncover a hidden organization of data objects are used to explore the strength and weaknesses of selected data analytical methods and to examine the resulting output. Where appropriate, ethical ramifications are discussed. 3 credit hours.
Offering Frequency: Spring
Grades: 22
Median GPA: A-
Mean GPA: 3.395
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