Course Material

1) The main learning source is the textbook by James, Witten, Hastie, Tibshirani (2013): "An Introduction to Statistical Learning". The textbook can be downloaded here: https://www-bcf.usc.edu/~gareth/ISL/

There are 15 hours of youtube videos by two of the authors of the book, Trevor Hastie an Rob Tibshirani -the inventors of statistical learning. Links to the lectures are copied into each module subpage.

2) All the lecture notes, recommended and compulsory exercises. Some classnotes that are made on the iPad and will also be uploaded.

3) Additional reading material will be clearly indicated in the modules and on the course page.

For more rigorous treatment and a good in-depth overview of statistical learning methods, I recommend the book "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman, freely available here.

The R course that you should work through in the first 1-2 weeks of the course can be found here:

https://digit.ntnu.no/courses/course-v1:NTNU+IMF001+2020/course/

You should be able to subscribe using your Feide account.

Many of you will be familiar with Python from earlier courses. Here is an attempt to translate large parts of the R code from our course book to Python:

https://github.com/JWarmenhoven/ISLR-python

Daesoo Lee is the Python expert in our course team, so please direct any respective questions directly to him, or post on the Mattelab forum.

The course was originally developed by Mette Langaas (original material: https://github.com/mettelang/StatisticalLearningSpring2019). Mette did a fantastic job and I am very thankful that I was allowed to modify and use her material.