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| Detailinformationen |
| Quellcurriculum |
Masterstudium Bioinformatics 2016W |
| Ziele |
(*)Knowledge of Bioinformatics and Machine Learning
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| Lehrinhalte |
(*)Classification, regression, kernels, sequence analysis, neuronal nets, support vector machines, hidden Markov models, clustering, principal component analysis, independent component analysis, projection methods, error models, optimization techniques, regularization, Bayes approach, hyper-parameter optimization, feature selection, statistical learning theory, etc.
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