The lecture deals with theoretical and practical concepts from the
fields of statistical learning and machine learning. The main focus is
on predictive modeling. The weekly tutorial applies these concepts and
methods to real examples for illustration purposes. You are expected to
work throughthe exercises for the tutorials. They will typically consist
of proofs of theory and programming tasks like the implementation of
algorithms. Selected topics are: learning theory, summary of simple
models: linear / logistic regression, LDA/QDA, KNN, decision trees:
CART, support vector machines, ensemble methods: bagging, random
forests, boosting model assessment, resampling, model selection variable
selection, machine learning in R (mlr package)
Language and slides are in English.
- Kursverantwortliche/r: Prof. Dr. Nadja Klein