```Some sample exam 1 questions:

1. Briefly define the following terms:

Concept Learning

Continuous-Valued Attribute

Discrete-Valued Attribute

Inductive Learning

The Inductive Learning Hypothesis

Version Space

Inductive Bias

Noise

Decision Tree

Entropy

Information Gain

Gain Ratio (in decision trees)

Overfitting

Artificial Neural Network

Linear Threshold Unit

Sigmoid Unit

2. Outline the four key questions that must be answered when designing a
machine learning algorithm.  Give an example of an answer for each question.

3. Define the following algorithms:

Find-S

List-Then-Eliminate (Version Space)

Candidate Elimination (Version Space)

ID3

Perceptron Training Algorithm (assuming linear artificial neurons)

Backpropagation (assuming sigmoidal artificial neurons)

4. For each of the algorithms above, show how it works on a specific problem
(examples of these may be found in the book or in the notes).

5. Why is inductive bias important for a machine learning algorithm?  Give
some examples of ML algorithms and their corresponding inductive biases.

6. How would you represent the following concepts in a decision tree:

A OR B
A AND NOT B
(A AND B) OR (C OR NOT D)

7. What problem does reduced-error pruning address?  How do we decide when
to prunce a decision tree?

8. How do you translate a decision tree into a corresponding set of rules?

9. What mechanism was suggested in class for dealing with continuous-valued
attributes in a decision tree?

10. What mechanism was suggested in class for dealing with missing attribute
values in a decision tree?

11. What types of concepts can be learned with a perceptron using linear units?
Give an example of a concept that could not be learned by this type of
artificial neural network.

12. A multi-layer perceptron with sigmoid units can learn (using an
algorithm like backpropagation) concepts that cannot be learned by
artificial neural networks that lack hidden units or sigmoid activation
functions.  Give an example of a concept that could be learned by such
a network and what the weights of a learned representation of this concept
might be.

13. An artificial neural network uses gradient descent to search for a local
minimum in weight space.  How is a local minimum different from the global
minimum?  Why doesn't gradient descent find the global minimum?

14. A concept is represented in C4.5 format with the following files.  The
.names file is:

Class1,Class2,Class3.  | Classes

FeatureA:  continuous
FeatureB:  BValue1, BValue2, BValue3, BValue4
FeatureC:  continuous
FeatureD:  Yes, No

The data file is as follows:

2.5,BValue2,100.0,No,Class2
1.1,BValue4,300.0,Yes,Class1
2.3,BValue3,150.0,No,Class3
1.4,BValue1,350.0,No,Class2

What input and output representation would you use to learn this problem
using an artificial neural network?  Give the input and output vectors for
each of the data points shown above.  What are the advantages and