English

Every spring semester

The course begins with an overview of basic data wrangling and visualisation. With a focus on the student's ability to identify and illustrate important features of the data.

Then important methods in statistical learning are introduced. Emphasis is given supervised and unsupervised learning. Issues arising from fitting and evaluating multiple models as well as the methods relationship to linear regression are discussed. Computer based labs and projects form an important part of the learning activities.The course concludes with a project where the students will select suitable methods to analyze a given data material.

* Basic methods for data handling and common visualisation methods for data
* Methods for unsupervised and supervised learning such as: clustering; hierarchical clustering; and regression and decision tree methods for classification and regression problems.
* Methods for model selection and validation such as: bootstrap, split of data into training and test, and cross-validation.

Describe different ways of aggregating, summarising and visualising data.
Explain the principles of supervised and unsupervised learning.
Explain the importance of evaluating models based on their predictive ability.

be able to wrangle, present and visualise data to highlight important features in a complex data material.
be able to use common methods for supervised and unsupervised learning.
be able to draw conclusions regarding a data material, based on results from classification and regression methods.
be able to use common method for evaluation of predictive ability on out-of-sample data.
present the analysis and conclusions of a practical problem in a written report.

Reflect over the limitations of the chosen model and method, as well as alternative solutions.
Reflect over the possible issues with fitting multiple models to the same data material.

Lectures

Laboratory exercises

Project

Oral exam

Written report

Passing grade on all written lab reports, passing grade on the final project report and oral presentation of the final project.

Failed, pass

Basic statistics

Basic statistics, some programming experience.

James, G., Witten, D., Hastie, T. & Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Springer, 2021. ISBN 9781071614174.

Studied together with FMSF90.

FMSF90F

2024-11-26

Maria Sandsten