new name of the course will be "Advanced topics in statistical learning and inference".

Course coordinator: Mette Langaas
Course team: Erlend Aune, Benjamin Dunn, Bo Lindqvist, Thiago Martins, John Tyssedal.

Goal: to give a broad introduction to principles and methods of contemporary statistics, see course description.

The main topic of the course is Statistical Learning, focussing on five specific topics.

• Part 1: Regularized linear and generalized linear models (Benjamin Dunn).
• Part 2: Smoothing and splines (Bo Lindqvist).
• Part 3: Experimental design in statistical learning (John Tyssedal).
• Part 4: Deep neural nets (Thiago Martins).
• Part 5: Active learning (Erlend Aune)

Teaching activities are planned in calendar weeks 2-13, and presentation of project work in weeks 14+15.

A larger project will count 30% of the grade, and is to be presented in the last two weeks (week 14+15) of the course (R or Python). The project work can be done in teams. There will be a final oral exam counting 70% of the grade.

The grade for this course is pass/fail, and 70/100 score is required to pass (this is the standard rule for PhD courses at NTNU).

Prerequisites:

• TMA4267 Linear statistical models
• TMA4295 Statistical inference
• TMA4300 Computer intensive statistical methods,
• TMA4315 Generalized linear models

Helpful knowledge

• TMA4268 Statistical learning
• TMA4180 Optimization

Programming/IT-knowledge

In addition good programming skills in either R or Python, and it is also preferable if you have some knowledge of commands in unix and the skills to be able to run a script on a computer cluster.

• Ingeborg Hem
• Even Moa Myklebust
• Tore Mo-Bjørkelund