Exercises the coming week
Week 18
Exercises from the book: 9.6 (in b only fit a tree). The ozone data are available here with some info here
Exercises from ISLR: Exercise 8.4, 8.8a-c,e (In 8.8b, use "text(tree_model, pretty = 0, cex = 0.7)" for a nicer plot)
Exam 2019: Problem 2.
Exercises for past weeks
Week 15
Extra exercises 12 and 13. You find the zip_nn.R file with examples on the use of nnet() and mlp() on the 2022 page: /studier/emner/matnat/math/STK2100/v22/r-scripts/.
- Solutions: extra_12.r and extra_13.r.
Week 14
Exam STK2100 2019: Exercises 1a,d,e and 3
Exam STK2100 2022: Exercise 1 (in d you do not need to consider the three last methods in the table)
- Solutions: /studier/emner/matnat/math/STK2100/oppgaver
Week 13
Exercises from the book: 5.4 and 5.7
- Solutions: this solution manual, and/or Vera's notes: esl_5_4.pdf esl_5_7.pdf.
Exercises from ISLR: Exercise 7.1, 7.9
- Solutions: https://rpubs.com/lmorgan95/ISLR_CH7_Solutions. Note that there are some similarities between ISLR 7.1 and ESL Ex. 5.1 from week 11 and R-code-examples in ISLR Ch. 7.8 Lab: Non-linear Modeling.
Extra exercise 7
- Solutions: Vinnie's solution, pp. 22-27.
Week 11
Exercises from the book: 4.2 and 5.1
- Solutions: See this solution manual, on which Vera's notes from 2024 is based (esl_4_2a.pdf, esl_5_1.pdf).
Exercises from ISLR: 4.9 and 4.14 (a-f)
- Solutions: islr2_4_9.pdf and Chapter 4.7 Lab: Classification Methods in the ISLR book for R-code-examples similar to 4.14.
Exam STK2100 2018: Problem 2
- Solution: /studier/emner/matnat/math/STK2100/oppgaver/STK2100_2018_fasit.pdf (in Norwegian).
Extra exercise 6
- Solutions: See Vera's solution, or Lars' solution from 2021.
Week 10
Exercises from the book: 4.1
- Solutions: Vera's notes, based on this solution manual.
Exercise from ISLR: 4.13 (without KNN)
- Solutions: islr2_4_13.r
Extra exercise: Modify the example with principal component (PC) regression in the R script r-code-week7.R, so that the numbers of principal components are selected through cross-validation instead of through separate training and test sets. Comment on the results.
- Solutions: r-code-week7-with-cv-for-pcr.r
Week 9
Exercises from the book: 3.2 and 3.29
- Solutions: esl_3_2.r (a simplified version of Lars' script from Spring 2023) and esl_3_29.pdf. More rigorous discussions are given in this solution manual (from page 33 and 57 for Exercise 3.2 and 3.29, respectively).
Exercises from ISLR: 3.9 a)-c) and e)-f)
- Solutions: rpubs.com/lmorgan95/ISLR_CH3_Solutions
Week 8
Exercises from ISLR: 3.3. 3.4, 3.6, and 3.7
- Solutions: rpubs.com/lmorgan95/ISLR_CH3_Solutions
Extra exercises 4 and 5 (extra4.r, advertising.r)
- Solutions: See Vinni's solutions and extra4_extended.r.
Week 6
Exercises from ISLR: 3.8 and 3.5
- Solutions: See Vinni's solutions, this one or that one.
Extra exercises (see link above): 1, 2 and 3
- Solutions: See Vinni's solutions, except from 2d, for which Vera presented this solution (where the lower bound is simply \(\sigma^2\), attained when \(f(x) = g(x)\)). For exercise 1, there are also Geir's solutions (excluding the R-part).
Week 5
Exercises from the book: 2.7
- Solutions: See pages 8-9 in /studier/emner/matnat/math/STK2100/v23/Exercise%20solutions/esl_ch2ex.pdf (note a typo in (a) for linear regression, where $\hat\beta$ should be $(X^TX)^{-1}X^Ty$ not $X(X^TX)^{-1}X^Ty$)).
Exercises from ISLR: 2.1, 2.2, and 2.8 (see the webpage for the book for downloading data; the easiest alternative is to install the ISLR library (through the command install.packages("ISLR")), make the library available (through the library("ISLR")), and then make the data available through data(College))
- Solutions: link to R-file.