(*)This practical course complements the lecture "Theoretical Concepts of Machine Learning" and aims at practicing the concepts and methods acquired in the lecture.
Subject
(*)
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
Criteria for evaluation
(*)Marking is based on homework
Methods
(*)Students are given assignments in 1-2 week intervals. Homework must be handed. Results are to be presented and discussed in the course.
Language
English
Study material
(*)Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
