Chapter 1 Exercises

1. Identify four real-life applications of supervised and unsupervised problems.
   • Explain what makes these problems supervised versus unsupervised.
   • For each problem identify the target variable (if applicable) and potential features.
2. Identify and contrast a regression problem with a classification problem.
   • What is the target variable in each problem and why would being able to accurately predict this target be beneficial to society?
   • What are potential features and where could you collect this information?
   • What is determining if the problem is a regression or a classification problem?
3. Identify three open source datasets suitable for machine learning (e.g., https://bit.ly/35wKu5c).
   • Explain the type of machine learning models that could be constructed from the data (e.g., supervised versus unsupervised and regression versus classification).
   • What are the dimensions of the data?
   • Is there a code book that explains who collected the data, why it was originally collected, and what each variable represents?
   • If the dataset is suitable for supervised learning, which variable(s) could be considered as a useful target? Which variable(s) could be considered as features?
4. Identify examples of misuse of machine learning in society. What was the ethical concern?