Examination arrangement

Course content

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.

Learning outcome

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.

Learning methods and activities

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 is usually given every second year, and only if a sufficient number of students register. If too few students register, the course is given as a guided self study.

Compulsory assignments

• Mandatory work

Recommended previous knowledge

TMA4267 Linear Statistical Methods, TMA4295 Statistical inference, TMA4300 Computer intensive statistical methods, TMA4268 Statistical learning, TMA4315 Generalized linear models - or equivalent knowledge. Good understanding and experience with R, or with Python, for statistical data analysis.

Course materials

The basis for the course is selected chapters from the The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics, 2009) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, but a lot has happened since 2009. In addition selected other material (chapters from books and journal articles) will be used. More detailed information will be given in the start of the course.