MA8701 General Statistical Methods - Spring 2019
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).
- TMA4267 Linear statistical models
- TMA4295 Statistical inference
- TMA4300 Computer intensive statistical methods,
- TMA4315 Generalized linear models
- TMA4268 Statistical learning
- TMA4180 Optimization
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