Assignment 0 Machine Learning

Assignment 0
Machine Learning

• Submisssion: Turn in as a PDF and the source code (R,Rmd,py,ipynb) on MyCourses

• Extra credit: Especially good questions or helpful answers on Piazza regarding the assignment earn
up to 5 points extra credit towards the assignment grade.
Problem 1 [33%]
What are the advantages and disadvantages of very flexible (vs less flexible) approach for regression or
classification?
1. When would be a more flexible approach preferable?
2. 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:
1. 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)
2. What are the minimum, maximum, and mean value of each feature? (in R use function summary() and
or range())
3. Produce a scatterplot matrix of all variables (in R use function pairs())
4. Produce a histogram of TV advertising (in R use function hist())
Problem 3 [34%]
Describe some real-life applications for machine learning.
1. Describe one real-life application in which classification combined with prediction may be useful.
Describe the response and predictors.
2. Describe one real-life application in which classification combined with inference may be useful. Describe
the response and predictors.
3. Describe one real-life application in which regression combined with prediction may be useful. Describe
the response and predictors.
4. 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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Hints
1. An easy way to launch help for any function in R, such as summary, is to execute: ?summary
2. 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/
3. 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
4. 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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