Variable selection using glmnet R package.

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

Enrol
9.9.2019 at 08:00 - 10.12.2019 at 23:59
Moodle
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Timetable

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

DateTimeLocation
Tue 29.10.2019
12:15 - 14:00
Thu 31.10.2019
10:15 - 12:00
Tue 5.11.2019
12:15 - 14:00
Thu 7.11.2019
10:15 - 12:00
Tue 12.11.2019
12:15 - 14:00
Thu 14.11.2019
10:15 - 12:00
Tue 19.11.2019
12:15 - 14:00
Thu 21.11.2019
10:15 - 12:00
Tue 26.11.2019
12:15 - 14:00
Thu 28.11.2019
10:15 - 12:00
Tue 3.12.2019
12:15 - 14:00
Thu 5.12.2019
10:15 - 12:00
Tue 10.12.2019
12:15 - 14:00
Thu 12.12.2019
10:15 - 12:00

Other teaching

29.10. - 10.12.2019 Tue 10.15-12.00
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.

Lectures and computer exercises.

Graded 1-5 based on exercises and exam.

Weekly exercises and exam.