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
- Regression Splines
- Tree-Based Methods: Random Forests and Boosting
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.
Lecture Notes and Exercises
All materials will be available on Moodle.
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.
Please write me at firstname.lastname@example.org if you are interested in attending the course. I will keep you posted regarding the organization of the course in April.
Time and Venue
The course schedule is:
- Lecture: Monday, 08:30-10:00, Online Video on Moodle
- Exercise class: Wednesday, 14:15-15:45, Online Video on Moodle, biweekly
MSc. Math, MSc. WiMa, MSc. Finance - elective course (4 Credit Points)
Analysis I+II, Elementary Statistics and Probability, Stochastic I, and Measure Theory