Messages
Here is a solution proposal for the exam
The project was a difficult one, but overall I was very satisfied with the answers you gave. In my own evaluation, the great majority got more than 30 out of 50 possible points, some very close to 50.
In the last part of a) it was easy to show that consecutive scores were orthogonal; the general case demanded a more complicated argument. Point b) was easy. Point c) was essentially a question of comparing two different notations. Many of you made point d) correctly.
In point e) the expression for the second regression coefficient was really messy, and to verify the stated formula for this coefficient in point f) was extremely difficult. Yet some of you were able to solve this point.
Most of you did the simulations in point g) OK. In point h) some of you found that the solution m=1 was best, and some found that the solution m=2 was best. It can be shown that when looking at the expected prediction error (a large training set and a very large test set), the so...
The curriculum list for the course is here
There will be no lecture 25.11. The last lecture will be 2.12, and then I will cover the Sections 7.6, 7.7, 7.8, 7.10 and 7.11
To all student working with the project in STK4030/STK9030:
The book is correct!! Sorry about the confusing e-mail sent out.
Lecture 11.11: Sections 6.5-6.9. While you are working with the project, there will be no exercices, only two hours' lecture.
The project with deadline November 23 at 10.00 is here
Exercises for 4.11: 5.9, 5.10, 5.11 (NB! drop 5.2 and 5.6)
Lecture 28.10: The rest of Section 5.4; Sections 5.5-5.7; the introduction to Section 5.9.
Lecture 14.10: The rest of section 4.4; section 4.5.
Exercises for 14.10: 3.13, 3.14, 3.16.
The following will be skipped from the curriculum: From Chapter 3: 3.3.3, 3.7, 3.8, 3.9; from Chapter 4: 4.3.2, 4.3.3.
Exercises for 7.10: 3.10, 3.11, 3.12.
Lecture 30.9: 3.4.3, 3.6, 3.8.3, 4.1, part of 4.2.
Lecture 23.9: section 3.5.2 plus two seminars on PLS and its population model.
Exercises for 23.9: 1) 3.4 page 95; 2) Define degrees of freedom for a predictor as in (3.60) page 77. Find the degrees of freedom for least squares with k inputs and for ridge regression.
The evaluation of the course will be project paper and written exam. The two parts will count equally on the grade, but both parts must be passed. The project assignment will be handed out in the beginning of November. As announced earlier, the written exam will be on 13. december at 14.30 (4 hours).