Course content
With successful completion of the module, the students possess the following knowledge and capabilities. They are able to
- understand and correctly apply basic concepts of machine learning
- analyse and formalize a machine learning problem
- distinguish between typical machine learning methods
- select a suitable method for a learning problem
- compare and judge machine learning methods wrt their capacity
- implement machine learning methods and apply them practically apply and parametrise respective tools
- judge strength and weaknesses of machine learning in applications
- recognize ethical issues in the application of machine learning
Fundamental principles and theories of machine learning und the underlying mathematical and statistical methods are introduced and learning problems are formalized. Important fundamental terminology, concepts and methods are treated, in particular for regression, among those are
- model selection, machine learning bias vs. parameter optimization
- training, test and validation
- generalization, overfitting, regularization
- linear regression, generalized linear models
- non-linear models, neural networks
- classification
- estimatimation, unbiased minimal variance estimators
- concept learning, decision trees, random forests
- methods of lazy learning
- unsupervised learning
- Gaussian mixtures, Gaussian mixture regression
- Unified Regression Model
