```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

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:

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?

```