### Timetable

### Description

Master's Programme in Mathematics and Statistics (MAST) is responsible for the course.

The course belongs to the Statistics study track of MAST.

The course is available to students from other degree programmes.

Basic studies of Bsc level statistics including linear models, linear algebra and R software.

Computational statistics

Knowledge of methods for high-dimensional inference problems and practical experience of solving them with computer.

Recommended time/stage of studies for completion: 1. or 2. year.

The course is lectured every second year.

Statistical inference when either the number of data units is large and/or each unit has been measured on a large number of variables.

1. Large scale inference (P-values, false discovery rates, Bayesian posterior probabilities).

2. Variable selection (stepwise regression, AIC, BIC; penalized regression with glmnet package using Lasso, ridge regression and elastic net).

3. Dimension reduction (principal components analysis, singular value decomposition).

Applications across modern data science.

Lecture notes during the course.

Selected sections from the books:

James, Witten, Hastie, Tibshirani: Introduction to Statistical Learning

Hastie, Tibshirani, Friedman: Elements of Statistical Learning

Lectures and computer exercises.

Graded 1-5 based on exercises and exam.

Weekly exercises and exam.