Messages
Here are the summary slides for the last lecture.
The slides for the lecture on Bagging and Random forests can be found here or under the Schedule, and with the R code: randomForests.R
The slides for the lecture 9th of November covering Boosting are available here (in the Exercises folder on the left hand side of the webpage) and adaboost.R and gradboost.R under R scripts.
The exam project with deadline Monday 23rd of November (13.00) is now available under the Exam project (on the right hand side of the web page).
Due to the exam project the lectures for the next three weeks will begin at 13.15 and end at 15.15.
The exercise for this period be a learning competition with last entry on Friday 20th of November, described under Weekly exercises. The winner will be announced in the last lecture 23rd of November.
No exercise class Nov 2nd. The lecture begins at 14.15.
The R script solutions to exercise 5.3 is available here, and the exercises for October 26th and November 2nd can be found under Weekly exercises.
The R script solutions to exercise 5.3 is available here, and the exercises for October 26th and November 2nd can be found under Weekly exercises.
The solution for the theoretical Exercise 4.2 and the R script for Exercise 4.9 are now available under Weekly exercises. As we did not manage to go through Exercise 4.2d) and e), we will discuss these in the exercise session next week.
The exam form for STK4030/STK9030 will be a project paper followed by a written exam. The project will be available from the 2nd of Nov with deadline 23rd of Nov. The written exam will be on the 11th of December.
The Extra exercise 3.4 and solutions for 3.3 can be found under Weekly exercises. After feedback from the student representatives I have also included an Exam problem for Sept. 21st, which you will be able to discuss in class. All previous exams with solutions are available here.
Thank you to Solveig and Simon for communicating feedback and opinions from the class regarding the lectures and exercises. The main issues:
- more theoretical exercises relevant for the written exam (from both old exams and the book). We will discuss and go through these exercises in class.
- the pace of the lectures is fine overall, but too slow for some and too fast for others.
- the book by HTF is great.
If you have other feedback, please contact Solveig (solveng"at"math.uio.no), Simon (simonlergenmuller"at"gmail.com) or myself.
Kristoffer Hellton
There will be a lecture on September 14th. We apologize for the earlier error in the lecture plan stating there would be no lecture next week.
The exercise for next week and the solutions for Extra exercise 3.2 are now found under Weekly exercises.
The exercise for Sept. 7th is now available (with some minor changes and correction from yesterday), together with the solution to Extra exercise 3.1 under Weekly exercises.
For those who are new to R or beginners, here are some useful links:
For a quick, but good, overview: A (very) short introduction to R
A short introduction: R for Beginners
A direct, but comprehensive guide: An introduction to R
Now the exercise for August 31st is available under Weekly exercises
Here are the solutions for the Ex 2.5 and 2.7, see also under Weekly exercises, and the R scripts for the Bayes boundary in Ex 2.2 and the PSA example shown in Chap. 3.1. The PSA data are available here.
The slides for the first lecture August 17th and a detailed plan for lectures on Ch. 3 in the weeks to follow are now available under Schedule.
Plan for each Monday from the 24th: We go through exercises 13:15-14:00, while ordinary lectures will be held 14:15-16:00.
The first lecture will begin 13:15.
Welcome to this course! We will be using the book Hastie, Tibshirani and Friedman: The Elements of Statistical Learning, Springer, 2nd edition (2009), available here or here.
The first lecture is already on Monday August 17th. This will be an introduction and overview of the content of the course.
I will update the course page with more information as soon as possible.
Kristoffer