Weekly plans and update for week 35

Dear all, welcome again

we hope your week started the best possible way and that you have had an enjoyable weekend.

What follows first is the the weekly reading assignments before the lab and online lecture sessions.

This week we will cover in more depth Linear Regression with examples. The explicit topics are

-Math of linear regression, Hastie et al chapters 3.1-3.3 and lecture slides 1-30 at https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html

- Statistics of linear regression and the bias-variance tradeoff, Hastie et al chapter 2.9 and lecture slides 51-82 https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html. Here we will start to discuss the bias-variance tradeoff and how to decide which model is the best. 

Background literature: Van Wieringen's paper https://arxiv.org/abs/1509.09169

and the text of Fahrmeir et al https://www.springer.com/gp/book/9783642343322 (you can download this for free with a UiO IP number), in particular chapters 2 and 3 for our beginning material. 

Concerning the lab sessions, you will receive an email later today about digital labs, their zoom links and more. We expect to discuss the exercises for week35. Not all of you have gotten admission to the course, this is also one of the reasons we use piazza for communication and github for course material. We have asked the department of Physics if it is possible to open up more places. Stay tuned. Hopefully we can sort this out to your benefit.

All the best from the teaching team.