Data Driven Multivariate Statistics

(Master, 2nd Semester, SS)

Content

The lectures cover non-trivial regressions (with correlated residuals, non-diagonal covariance matrix, kernel regression, etc), Bayesian regressions, classification methods (Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, Boosting, Bagging, etc), Missing Data Analysis (Missing at random, EM algorithms, etc), neural networks with the introduction of deep learning. The lectures will be held in the PC Pool so that all procedures in Statistical Software R are directly implemented and applied to the true records. 

Details

• The course is offered online this semester.
• To get the credit points, the participants have to attend the written exam (120 minutes) successfully.
• The PCs in the FAL / 002 are available during the lectures. All participants can also use their own laptop with installed R Software (open source) (please note the GNU GENERAL PUBLIC LICENSE).]
• If you have any questions, please contact Bingyu Wang.
• The course language is English.

News

• General information about the semester process at the chair you can find here.
• All actual information, lecture scripts, lecture videos can be found on OPAL.
• Please register for the course on OPAL!

Schedule

Topics

(may change during the semester)

• Graphical Representation of Numerical Data.

• Decision Trees.

• Cluster Analysis.

• Linear Classifier.

• Logistic Regression.

• Maximum Margin Classifiers and the SVM.

• Missing Data.

• Association Analysis.

Literature

• Hastie, T., Tibshirani, R., Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition), Springer Verlag.
• Backhaus, K., Erichson, B., Plinke, W., Weiber, R. (2008), Multivariate Analysemethoden: Eine anwendungsorientierte Einführung (12. Auflage), Springer Verlag.
• Härdle, W., Simar, L. (2015), Applied Multivariate Statistical Analysis (4th edititon), Springer Lehrbuch.
• Härdle, W., Hlavka, Z. (2007), Multivariate Statistics: Exercises and Solutions, Springer Lehrbuch.