Advanced statistical methods in inference and learning

In this course we discuss principles and methods of statistical inference and learning. The topics of the course build and expand on the contents of the courses listed under "Recommended previous knowledge".

1. Knowledge. Understand and explain central theoretical aspects in statistical inference and learning. Understand and explain how to use methods from statistical inference and learning to perform a sound data analysis. Be able to evaluate strengths and weaknesses for the methods and choose between different methods in a given data analysis situation. 2. Skills. Be able to analyse a dataset using methods from statistical inference and learning in practice (using R or Python), and discuss the choices taken and the results found. 3. Competence. The students will be able to participate in scientific discussions, and read research presented in statistical journals. They will be able to participate in applied projects, and analyse data using methods from statistical inference and learning.

Lectures, alternatively guided self-study. Practical compulsory group project in data analysis (application of course theory using R or Python) and compulsory oral group presentation of a research article/topic.

The course will be taught as needed. If there are few PhD students, the course is only given as a guided self-study.