Beskjeder

Since there are no exercise classes on Monday 14.04 and 21.04 dues to the Easter holidays, there are no weekly exercises those weeks.

The deadline is Thursday April 24 at 14:30. 

Good luck!

Note that there was an error in the R code for Chapter 7, in the file r-code-week6.r where the training error error.train.all was computed on line 150 in the code. It should have been computed as the mean of the squared residuals and not the sum. This is now corrected in the available file. Since you were supposed to use that code as a source of inspiration in the first mandatory assignment, many of you have probably copied this error. I appologise for this! This will of course not affect whether you pass the assignment or not. 

Note that it is allowed to cooperate on the mandatory assignment, but you should all hand in a separate answer. You may of course ask questions about the assignment during the weekly exercise hours on Mondays. Further, many of the problems require programming. You do not have to use R, but if you use another programming language, we might not be able to help you. Also note that there is a lot of relevant R examples in the R code posted in the time table for January 28, February 4 and February 19.

Good luck!

Due to an intensive course, that uses our room, there is no teaching next week, and therefore no weekly exercises either. I encourage you yo use the time to catch up with the material covered so far (Ch. 2, first half og Ch. 3 and ch. 7), as well as the weekly exercises given so far.

The link to the book ISLR, from which some of the weekly exercises are taken, has now been substituted with a link that gives access both to the book itself, but also R files and data sets that you might need. These are found under "Resources".

The teaching will each week consist of 4 hours of lectures (Tuesdays and Wednesdays), as well as 2 hours of exercise classes on Mondays, where you can get help solving the weekly exercises. Note that there are no exercises in week 4, and hence, no exercise classes on Monday January 20th.

On the semester page, plans for the lectures will be published in the schedule, and the weekly exercises will be published on "Weekly exercises' under 'Exercises'.

We will use the text book: Hastie et al (2009): Elements of Statistical learning. The book James et al (2013): An introduction to Statistical learning - with applications in R can be used as supplementary material. The latter is an easier version of the former, with less mathematical details. Both books are available online. Further, the course will be a mix of statistical/mathematical theory and applied work on the computer. We will use R extensively....