Martin-Luther-Universität Halle-Wittenberg



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Statistical Machine Learning

This course complements the Master's curriculum with models, methods and concepts mainly from the realm of machine learning. We will study intermediate to advanced models (e.g. trees, neural networks, ensembles), feature representations (e.g. kernels, adaptive basis functions) and strategies to ensure generalization (e.g. validation, regularization).

Participants are required to bring working knowledge in R or the willingness and effort to obtain that on the side. If you feel not comfortable with R, you may join the Bachelor module "Data Science I" in the first month of the winter semester to catch up (no credit).

Statistical Learning

Statistical Learning

Course Structure

This course is hands-on in that the weekly lecture is complemented by a weekly R Tutorial that we use to discuss, implement and practice the current topic.


The content is still tentative an subject to change.

Week 1Empirical Loss Minimization
Week 2Tree models
Week 3Features I: PCA
Week 4Features II: Clustering
Week 5Features III: Kernels & Adaptive Basis Functions
Week 6Neural Networks I
Week 7Neural Networks II
Week 8Generalization, Statistical Learning Theory
Week 9Validation, Information Criteria
Week 10Regularization, Model Selection
Week 11Ensembles: Bagging
Week 12Ensembles: Boosting
Week 13TBD (Ensembles: Mixtures of Experts)
Week 14TBD
Week 15TBD


There will be a project + presentation due at the end of the semester. Details tba.

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