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Inhalt des Dokuments

Geo Data Science

Course details
Term
SPW
Type
Lecturer
Person in charge
2nd. (SS)
4
Lecture
Exercises
Prof. Dr.-Ing. Martin Kada [1]
Prof. Dr.-Ing. Martin Kada [2]

Course content

  • Exploratory thematic and spatial data analysis (with Python)
  • Statistical analytics
  • Correlation, data manipulation and cleaning
  • Feature extraction from geographical data
  • Supervised vs. unsupervised learning
  • Linear and polynomial regression
  • Regularized linear models
  • Logistic and multinomial logistic regression
  • Cost functions
  • Model training and fine-tuning
  • Gradient descent, learning curves, performance measures
  • Support vector machines
  • Decision trees, random forests, ensemble learning, dimensionality reduction
  • Segmentation and clustering (k-means, hierarchical clustering, DBSCAN)
  • Privacy and ethics in data science
  • Python numerical, scientific, and machine learning libraries

Didactic concept

  • Lecture (45%)
  • Hands-on exercises (45%)
  • Independent reading (10%)

Quicklinks

  • Institut [3]
  • ISIS [4]
  • Campus Map [5]
  • Matriculation Office [6]
  • Examination Office [7]
  • Register of lectures [8]
  • Dates and Deadlines [9]
  • News [10]
  • 3D Campus [11]
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