====== 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).

**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. 

====== Reference group ======

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