Theoretical Concepts of Machine Learning (1UE)


 
Course no.: 365.042 (group 1) / 365.100 (group 2)

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