Statistical Learning

This course covers topics of statistical learning in a mathematical and economical approach.

Specific topics are

• Linear Regression
• Classification
• 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.

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.