Statistical learning with high-dimensional data 7.5 credits

About the course

This course provides comprehensive knowledge, both regarding breadth and depth, about data science and statistical learning. In the course, both traditional and state of the art methods and algorithms in these fields are discussed. The related fundamental theories are also covered. After passing the course, the students should have a strong ability to solve problems through data. Meanwhile, students are also expected to have a strong self-study ability for understanding and learning any newly developed methods and algorithms.

Module 1 (3hp): Theory
The course gives an overview of important techniques and concepts in statistical learning. Supervised learning techniques, both classical as well as more modern, are treated. In particular, ensembling (e.g. random forests), deep neural networks and regularized linear regression are treated. Additionally, techniques for unsupervised learning (clustering) as well as dimension reduction are treated. The course especially covers mathematical theory about machine learning in general as well as the presented methods in particular.

Module 2 (4.5hp): Computer labs
The module covers the analysis of several data sets, using the statistical methods that are included in the course. The analyses are conducted in one of the the programming languages R or Python. In the module, students write thorough reports of the analyses and the results from them.