1. What’s an attribute? What’s a data instance?

1. What’s      noise? How can noise be reduced in a dataset?
2. Define      outlier. Describe 2 different approaches to detect outliers in a dataset.
3. Describe      3 different techniques to deal with missing values in a dataset. Explain      when each of these techniques would be most appropriate.
4. Given      a sample dataset with missing values, apply an appropriate technique to      deal with them.
5. Give 2      examples in which aggregation is useful.
6. Given      a sample dataset, apply aggregation of data values.
7. What’s      sampling?
8. What’s      simple random sampling? Is it possible to sample data instances using a      distribution different from the uniform distribution? If so, give an      example of a probability distribution of the data instances that is      different from uniform (i.e., equal probability).
9. What’s      stratified sampling?
10. What’s      “the curse of dimensionality”?
11. Provide      a brief description of what Principal Components Analysis (PCA) does.      [Hint: See Appendix A and your lecture notes.] State what’s the input and      what the output of PCA is.
12. What’s      the difference between dimensionality reduction and feature selection?
13. Describe      in detail 2 different techniques for feature selection.
14. Given      a sample dataset (represented by a set of attributes, a correlation      matrix, a co-variance matrix, …), apply feature selection techniques to      select the best attributes to keep (or equivalently, the best attributes      to remove).
15. What’s      the difference between feature selection and feature extraction?
16. Give      two examples of data in which feature extraction would be useful.
17. Given a      sample dataset, apply feature extraction.
18. What’s      data discretization and when is it needed?
19. What’s      the difference between supervised and unsupervised discretization?
1. Given      a sample dataset, apply unsupervised (e.g., equal width, equal frequency)      discretization, or supervised discretization (e.g., using entropy).
2. Describe      2 approaches to handle nominal attributes with too many values.
3. Given      a dataset, apply variable transformation: Either a simple given function,      normalization, or standardization.
4. Definition      of Correlation and Covariance, and how to use them in data pre-processing      (see pp. 76-78).