After successful completion of the course, students are able to (i) apply modern regression/statistical learning methods to build predictive models, (ii) select and validate statistical learning models, (iii) assess model fit and error and (iv) use the R language for modern regression and data analysis.
Theoretical and practical examples using R.
The prerequisite for the course is
Continuous assessment via oral examination and regular homework tasks throughout the semester. A data analysis project, that will be presented at the end of the semester, will count for 1/3 of the grade.
Basic probability and statistics; Linear algebra; Econometrics 1: Linear Models.
