Variable selection using glmnet R package.

How to do statistical inference with large numbers of variables encountered across modern data science applications?

Enrol
23.10.2017 at 09:00 - 13.12.2017 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 30.10.2017
10:15 - 12:00
Tue 31.10.2017
12:15 - 14:00
Mon 6.11.2017
10:15 - 12:00
Tue 7.11.2017
12:15 - 14:00
Mon 13.11.2017
10:15 - 12:00
Tue 14.11.2017
12:15 - 14:00
Mon 20.11.2017
10:15 - 12:00
Tue 21.11.2017
12:15 - 14:00
Mon 27.11.2017
10:15 - 12:00
Tue 28.11.2017
12:15 - 14:00
Mon 4.12.2017
10:15 - 12:00
Tue 5.12.2017
12:15 - 14:00
Mon 11.12.2017
10:15 - 12:00
Tue 12.12.2017
12:15 - 14:00

Other teaching

01.11. - 29.11.2017 Wed 14.15-16.00
13.12.2017 Wed 14.15-16.00
Matti Pirinen
Teaching language: English

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.

Weekly exercises and exam.

Lectures and computer exercises.

Graded 1-5 based on exercises and exam.

Weekly exercises and exam.