1300182202
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Content
- Axioms of Probability Theory; random variables, important distributions
- Bayesian inference
- Linear regression, non-linear regression
- Regularized regression to fit high-dimensional data
- Hypothesis testing: fundamental concepts
- Parametric and non-parametric tests
- Classification
- Cluster analysis
- Model selection
Goal
After completion of this module, the students understand fundamental concepts of stochastics, and are able to relate them to concrete problems. They understand and are alert of possible pitfalls such as overfitting, multiple comparisons, or susceptibility to outliers. They know and are able to apply basic countermeasures and they have access to more advanced literature on the subject. Students are familiar with relevant high-level languages and statistical programming libraries, and know how to apply them to real-world data provided in the exercises.
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