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Inhaltsbereich

Probst

  • Statistical Learning/Machine Learning
  • Tree-based Methods (e.g. Random Forest)
  • Ensemble Methods (e.g. Bagging, Boosting, Stacking)
  • Parametersettings and -tuning of Machine Learning Algorithms
  • Selection of Benchmark Datasets
  • Benchmarkexperiments
  • Multitarget Problems and Multilabel Classification
  • Quantile, Distribution and Density Estimation with Tree-based Methods
  • Computational Aspects of Machine Learning
  • Statistical Software Development (R, Python, Matlab, SAS, SQL, ...)