. What is similarity matching?

3. Discuss how we can show similarity on a two dimensional graph.

4. How can we make similarity matching more or less sensitive to outliers?

5. What is the voting method used in similarity matching?

6. How are weights added to the voting method?

7. What is clustering?

8. Why is clustering referred to as iterative?

9. Why do we sometimes measure surrogates rather than we'd really like to measure?

10. Discuss potential problems with using classification accuracy as the main metric for classification.