Person(s) in Charge
Prof.Dr. Peter Ruckdeschel & M.Sc. Tino Werner
Topic
Statistical Learning: Methods and Applications
Type
lecture (3) + exercise course (1)
KP
6KP
Workload
180h
Hours/Week
4.00h
Exam Type
extended exercise in R / homework project
Access Limits
None
Prerequisites
builds up on basic courses in Statistics/Stochastics
Location
Uni Oldenburg, Mo, 10:00-12:00, Tu, 08:00-10:00 at W01 0-012
Description
- Prediction
- linear and generalized linear models with more predictors than observations (lm, glm)
- regularized regression: enet, LASSO
- SVM regression
- Classification
- linear and quadratic discriminant analysis (LDA/QDA)
- Support Vector Classification
- CART
- Resampling / Ensemble Methods
- Bagging
- Boosting
- Random Forests
- Outlook: Ranking, Online Learning
All methods are introduced together with implementations in R and illustrated at applications
Semester