The final exam will be cumulative.

For sample questions for the material covered for Midterm 1 you should look at this page. For sample questions for the material covered for Midterm 2 you should look at this page.

Sample questions concerning material covered since Midterm 2:

1. BRIEFLY define the following terms and give an example of how each term is used:
   • Decision Tree
   • Entropy of a set of examples
   • Information gain of a set of examples
   • Conditional probability
   • Conditional independence
   • Full joint probability distribution
   • Bayes rule
   • Bayes network
2. Define the ID3 algorithm for learning a decision tree.
3. For some set of examples with some features calculate the information gain for each feature (show the entropy values needed).Which of the features would be chosen in learning a decision tree?
4. In a decision tree it is sometimes preferrable not to include a set of decisions that complete separates a set of points. Why? Give an example that illustrates your point. How do decision tree learning algorithms learn in such cases?
5. How are features with more than two values captured as decisions in decision trees? How about continuous features?
6. Why is probability often used in reasoning in artificial intelligence? Give an example of a situation where a probabilistic representation would be preferrable to a pure logical representation.
7. Given some full joint probability distribution give two examples of conditional probabilities that can be determined from that distribution.
8. How does the chain rule work in evaluating probability? Give an example and show how conditional independence affects this rule.
9. What are the advantages and disadvantages of using a full joint probability distribution? How do Bayes networks address the disadvantages?
10. Given a Bayes network show how to estimate a probability using that network.
11. What is Bayes rule? Give an example demonstrating how that rule can be applied.