Sample questions for midterm 2:

1. Briefly define the following terms:

   Eager learning

   Lazy Learning

   Curse of dimensionality

   kd Tree

   Single Point Crossover

   Two Point Crossover

   Uniform Crossover

   Point Mutation

   Inverted Deduction

   Unsupervised learning

   Clustering Algorithm


   Control Learning

   Delayed Reward

   Discounted Future Reward

   Markov Decision Process

   Bayes Theorem

   Maximum a posteriori hypothesis

   Maximum likelihood hypothesis

   Bayes optimal classifier

   Gibbs classifier

   Bayes network

   PAC learning

   e-exhausting a Version Space

   Shattering a Set of Instances

   Vapnik-Chervonenkis Dimension

   Linear programming

   Slack variable

   Margin (of a SVM decision surface)

   Support vector

   Domain theory




2. How does a k-Nearest Neighbor learner make predictions about new data points?
   How does a distance-weighted k-Nearest Neighbor learner differ from a
   standard k-Nearest Neighbor learner?  What is locally weighted regression?

3. How does a Radial Basis Function network work?  How does a kernel function

4. How are concepts represented in a genetic algorithm?  Give an example of
   of concept represented in a GA.

5. What operators are used in a genetic algorithm to produce new concepts?
   Give an example of a mechanism that can be used to judge a GA concept.

6. Give pseudo-code for a general genetic algorithm.  Make sure to outline
   the way concepts are represented, the operators used to create new
   concepts, how concepts are chosen to reproduce, and how concepts are

7. Give two different mechanisms for selecting which members of a GA
   population should reproduce.  What are the advantages and disadvantages of
   your mechanisms?

8. How does genetic programming work?  How is a genetic program defined?
   What genetic operators can be applied to a genetic program?

9. How does the sequential covering algorithm work to generate a set of
   rules for a concept?

10. How does FOIL work to generate first-order logic rules for a concept?

11. What does it mean to view induction as inverted deduction?  Give a
    deduction rule and explain how that rule can be inverted to induce new

12. What are the two main approaches for generating clusters?  Explain in
    general terms how these approaches work.

13. List two methods that could be used to estimate the validity of the
    results of a clustering algorithm.  Explain how these methods work.

14. Explain how the following clustering methods work:

    Agglomerative Single Link

    Agglomerative Complete Link


15. A distance measure is important both in memory-based reasoning methods
    such as the k-nearest neighbor method and in clustering.  Why is it so
    critical in these methods.  In which is it possible to "learn" to do
    a better job of measuring the distance between points?  Why?

16. Give pseudo-code for the learning cycle of a Q learner.  What is the
    update rule for a deterministic world?  How about a non-deterministic

17. How are the V(s) and Q(s,a) functions related in Q learning?  What are
    the advantages of using the Q function over the V function?

18. What is Bayes theorem?  Discuss two examples showing how Bayes theorem
    can be used to justify approaches to learning.  Also, discuss an example
    of a learning method based on Bayes theorem.

19. What is a Bayesian Belief network?  Give an example of such a network.
    What are the advantages of a Bayesian network over a naive Bayes learner?

20. Explain the fundamental difference between the Bagging and Ada-Boosting
    ensemble learning methods?  How do these notions relate to the concept of
    generating a good ensemble?  What are the advantages and disadvantages of
    each method?

21. Give an example of a domain theory expressed as predicates.  How would
   that domain theory be converted into a corresponding neural network
   by KBANN (show the structure and weights of the resulting network).
   What is the main advantage of the KBANN approach?

22. What is a hybrid learning algorithm?  Give three examples of hybrid learning
   methods, explain how each works, and discuss the advantages of each.

23. How is a problem phrased as a linear program in a support vector machine?
    Give an example.  What are slack variables used for and how are they
    represented in the linear program?

24. Explain the concept of a Kernel function in Support Vector Machines.
    Why are kernels so useful?  What properties should a kernel have to be
    used in an SVM?