|Type:||MSc. Math, MSc. WiMa, MSc. Finance - elective course|
The exercise class takes place every two weeks.
|Time and Venue:||The course schedule is:|
- Lecture: Monday, 16:00-18:00, He18 - 1.20
- First Lecture: 15/10/2018
- Exercise class: Thursday, 08:00-10:00, He18 - 1.20, biweekly
- First Exercise class: 08/11/2018
oral exam of 20 minutes.
To participate in the oral exam, you have to register at campusonline.uni-ulm.de.
Analysis I+II, Elementary Statistics and Probability, Stochastic I, and Measure Theory.
By attending the course you will
- understand and master fundamental principles and modelling techniques for the analysis of regression and classification problems
- Gain or deepen, respectively, model assessment and inference techniques for linear and non-linear models.
- Exercising the acquired techniques by means of real data sets and the R software.
This course covers topics of statistical learning in a mathematical and economical approach.
Specific topics are
- Linear Regression
- Model assessment, selection and inference: cross-validation, bootstrap
- Regularization methods: Ridge and Lasso regression
- Overview of non-linear models: splines, support vector machines and neural networks
|The course follows the following books: |
- T. Hastie, R. Tibshirani & J. Friedman, The Elements of Statistical Learning: data mining, inference and prediction, 2nd edition, Springer, 2009.
- G. James, D. Witten, T. Hastie & R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.
- W.H. Green, Econometric Analysis (Seventh Edition), Pearson, 2012.
- D.W. Hosmer, S. Lemeshow, R.X. Sturdivant, Applied Logistic Regression (Third Edition), 2013.
- G. Casella, R.L. Berger, Statistical Inference (Second Edition), 2001.
- B. Efron and R.J. Tibshirami, An Introduction to the Bootstrap, Chapman & HALL/CRC, 1994.