Description
Problem 1 [33%]
What are the advantages and disadvantages of very flexible (vs less flexible) approach for regression or classification?
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When would be a more flexible approach preferable?
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What about a less-flexible approach?
Problem 2 [33%]
Install and learn to use R (https://www.r-project.org/) or Python, read the labs in Chapter 2 of the textbook. We recommend that you use R Notebooks of RStudio to typeset homeworks. Jupyter is a comparable tool for Python. Use Python or another tool (like MATLAB or Julia) if you have some experience and you will not need help from the TA/instructor. Then:
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Download the advertising dataset (Advertising.csv) from http://www-bcf.usc.edu/~gareth/ISL/data. html and load it into R/Python (use function read.csv() in R or Pandas in Python)
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What are the minimum, maximum, and mean value of each feature? (in R use function summary() and or range())
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Produce a scatterplot matrix of all variables (in R use function pairs())
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Produce a histogram of TV advertising (in R use function hist())
Problem 3 [34%]
Describe some real-life applications for machine learning.
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Describe one real-life application in which classification combined with prediction may be useful. Describe the response and predictors.
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Describe one real-life application in which classification combined with inference may be useful. Describe the response and predictors.
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Describe one real-life application in which regression combined with prediction may be useful. Describe the response and predictors.
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Describe one real-life application in which regression combined with inference may be useful. Describe the response and predictors.
Optional Problem O3 [39%]
This problem can be substituted for Problem 3 above, for 5 points extra credit. At most one of the problems 3 and O3 will be considered.
Read sections 1.2, 1.2.1, 1.2.2 in [Bishop, C. M. (2006). Pattern Recognition and Machine Learning] and solve Exercise 1.5.
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An easy way to launch help for any function in R, such as summary, is to execute: > ?summary
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See http://rmarkdown.rstudio.com/pdf_document_format.html for how to generate a PDF from an R notebook in R-studio. You will also need to install LATEXwhich you can get from https://www. latex-project.org/get/
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For more advanced (and prettier?) plotting capabilities, see the package ggplot: http://ggplot2. tidyverse.org/ and https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf
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If you think you may struggle with R, consider signing up for MATH 759, a 1-credit online introduction to R.
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