Motivation:

This practical course complements the lecture Theoretical Concepts of Machine Learning and aims at practicing the concepts and methods acquired in the lecture.

Topics:

• Generalization error
• Bias-variance decomposition
• Error models
• Model comparisons
• Estimation theory
• Statistical learning theory
• Worst-case and average bounds on the generalization error
• Structural risk minimization
• Bayes framework
• Evidence framework for hyperparameter optimization
• Optimization techniques
• Theory of kernel methods