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The exam set and solution are now published further down on the page.

- The only permitted aid on the exam is an approved calculator; you will find the list of approved calculators here: /studier/eksamen/hjelpemiddel/mn-math-kalkulatorer.html.

- The formula sheet will be an appendix to the exam paper, and you cannot bring your own copy. You should however take a look at it before the exam; you will find it further down on this web-page. 

- Results in the formula sheet can be used without need for being shown, unless you are expressly instructed to do so in the exam problem.

- The exam will consist of sub-problems (a, b, c, ...), that may be of different length and difficulty, but they all count the same on the grade.

- Some of the sub-problems may contain several questions. To get a full score, you must answer all of them.

- A correct answer is not enough in itself ...

Solutions to the three exams we went through in the last lectures are now available. Note that these are more complete and representative of what a solution to an exam should look like than the notes made during the lectures.

The lecture on Wednesday was the last this semester. You may however come to my and ask me questions:

- Tuesday May 20 10-12

- Tuesday May 27 10-12 

in my office, room 816 in NHA.

I also want to take the opportunity to thank those of you who have attended the lectures for showing so much interest for the course and asking me so many questions!  

There are no new exercises for tomorrow Monday 12. May. I will be there and answer questions on previous exercises and/or the mandatory assignment. 

Vera

Note the Wednesday lecture (May 7) is moved to Monday 5 14:15-16:00 in VB1, whereas the exercise class Monday 5 is moved to Wednesday 7 14:15-16:00 in VB1.

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....