Some sample exam 2 questions:

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

   The Baldwin Effect

   Inverted Deduction

   Unsupervised learning

   Clustering Algorithm

   Dendogram

   Control Learning

   Delayed Reward

   Discounted Future Reward

   Markov Decision Process

   Analytical Learning

   Chunking

   Impasse

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
   work?

4. What is Case-Based Reasoning (CBR)?  Give an example of how CBR might be
   used to solve a new problem.

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

6. 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.

7. 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
   evaluated.

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

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

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

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

12. 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
    rules.

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

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

15. Explain how the following clustering methods work:

    Agglomerative Single Link

    Agglomerative Complete Link

    K-Means

16. 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?

17. 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
    world?

18. 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?

19. How does Temporal Difference Learning relate to Q learning?

20. What is the difference between analytic or speedup learning and inductive
    learning?  Give an example of each type of learning.

21. Give an example of an explanation of a concept that might be used by
    an explanation-based learner.  What could be learned from this explanation?
    How does the concept of operationalization relate to what is learned?

22. What are some of the problems that can result from using a domain theory
    in an explanation-based learner?  How can we address these problems?

23. What is the utility problem in analytical learning?  How can we define
    utility?

24. What does it mean when we say that PRODIGY learns control knowledge?
    What is the advantage of learning control knowledge over adding new rules
    to a domain theory?

25. How does chunking work in SOAR?  When does a SOAR system try to create
    a chunk?