| Lehrinhalte |
(*)Knowledge of, understanding in, and approaches to following topics:
Machine Learning: Supervised Techniques
Classification, regression, kernels, sequence analysis, neuronal nets, support vector machines, regularization, Bayes approach, hyper-parameter optimization, feature selection,generalization error, model selection
Data Analysis:
Box plot, scatter plot, clustering, principal component analysis, regression, variance analysis, feature selection, classification
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