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Important Questions and Answers: Artificial Intelligence - Uncertainty

**1.** **What is learning?**

Learning
takes many forms, depending on the nature of the performance element, the
component to be improved, and the available feedback.

**2.
****What are
the types of Machine learning?**

a)
Supervised

b)
Unsupervised

c)
Reinforcement

**3.
****Define
the following:**

a)
__Classification:__

Learning
a discrete-valued function is called classification.

b) __Regression:__

Learning
a continuous function is called regression.

**4.** **What is inductive learning?**

The task
is to learn a function from examples of its inputs and outputs is called
inductive learning.

**5.** **When will a learning problem is said to be
realizable or unrealizable?**

A
hypothesis space consisting of polynomials of finite degree represent
sinusoidal functions accurately, so a leaner using that hypothesis space will
not be able to learn from sinusoidal data.

A
Learning process is realizable if the hypothesis space contains the true
function, otherwise it is unrealizable.

**6.** **What is decision tree?**

A
decision tree takes as input an object or situation described by a set of
attributes and returns a “decision”, the predicted output value for the input.
The input

can be
discrete or continuous.

**7.** **Define goal predicate.**

To define
the goal predicate should have the following list of attributes.

a)
Alternate

b)
Bar

c)
Fri/Sat

d)
Hungry

e)
Patrons

f)
Price

g)
Raining

h)
Reservation

i)
Type

j)
Wait Estimate

**8.** **Define the kinds of functions. (Apr/May
2005)**

The kinds
of functions are a real problem.

__Parity
function:__

Returns 1
if and only if an even number of inputs are 1, then an exponentially large
decision tree will be needed.

__Majority
function:__

Returns 1
if more than half of its inputs are 1.

**9.** **Define training set.**

The
positive examples are the ones in which the goal *WillWait* is true (X1,

X3,…….);
the negative examples are the ones in which it is false (X2, X5,…..). The

complete
set of examples is called the training set.

**10. How do you assess the performance of the
learning algorithm?**

A
learning algorithm is good if it produces hypotheses that do a good job of
predication the classifications of unseen examples. We do this on a set of
examples known as the test set. It is more convenient to adopt the following
methodology:

a) Collect a large set of examples.

b)
Divide it into two disjoint sets: the training set
and the test set.

c)
Apply the learning algorithm to the training set,
generating a hypothesis h.

d)
Measures the percentage of examples in the test set
that are correctly classified by h.

e)
Repeat steps 1 to 4 for different sizes of training
sets and different randomly selected training sets of each size.

**11. Define Overfitting.**

Whenever
there is a large set of possible hypotheses, one has to be careful not to use
the resulting freedom to find meaningless “regularly” in the data. This

problem
is called overfitting.

**12. What is ensemble learning?**

The idea
of ensemble learning methods is to select a whole collection, or ensemble, of
hypotheses from the hypothesis space and combine their predictions.

**13. Define Weak learning algorithm.**

If the
input learning algorithm L is a week learning algorithm which means that L
always returns a hypothesis with weighted error on the training set that is
slightly better than random guessing.

**14. Define computational learning theory. (Apr/May 2008)**

The
approach taken in this section is based on computational learning theory, a
field at the intersection of AI, statistics, and theoretical computer science.

**15. What do you mean by PAC-learning algorithm?
(Apr/May 2008)**

Any learning
algorithm that returns hypothesis that are probably approximately correct is
called a PAC-learning algorithm.

**16. What is an error?**

The error
of a hypothesis *h* with respect to the
true function *f* given a distribution
D over the examples as the probability that h is different from f on an
example.

Error (h)
= p (h(x) = f(x)|x drawn from D)

**17. Define Sample Complexity.**

The
number of required examples, as a function of *E* and, is called the sample complexity of the hypothesis space.

**18. Define neural networks.**

A neuron
is a cell in the brain whose principal function is the collection, processing
and dissemination of electrical signals.

**19. Define units in neural networks.**

Neural
networks are composed of nodes or units connected by directed links. A link
from unit j to unit i serve to propagate the activation aj from j to i.

**20.
****Mention
the types of neural structures.**

a.
Feed-forward networks

b.
Cyclic or recurrent network

**21.
****Define
epoch.**

Each
cycle through the examples is called an epoch. Epochs are repeated until some
stopping criterion is reached typically that the weight changes have become
very small.

**22. What do you mean by Bayesian learning? (Nov/Dec
2005)**

Bayesian
learning methods formulate learning as a form of probabilistic inference, using
the observation to update a prior distribution over hypotheses. This approach
provides a good way to implement Ockham’s razor, but quickly becomes
intractable for complex hypothesis spaces.

**23. What is reinforcement?**

The
problem is this without some feedback about what is good and what is bad, the
agent will have no grounds for deciding which move to make. The agent needs to
when it loses. This kind of feedback is called a reward, or reinforcement.

**24. Define passive learning.**

The
agent’s policy is fixed and the task is to learn the utilities of states, this

could
also involve learning a model of the environment.

**25.
****Define
the following:**

a.
__Utility-based agent:__ Learns a
utility function on states and uses it to select actions that maximize the
expected outcome utility.

b.
__Q-learning agent:__ Learns an action-value function
or Q-function, giving the expected utility of taking a given action in a given
state.

c.
__Reflex agent:__ Learns a policy that maps
directly from states to actions.

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