EXCEL ENGINEERING COLLEGE
(Autonomous)
B.Tech – Information Technology
IV SEMESTER
20IT402 COMPUTATIONAL INTELLIGENCE
Regulations 2020
Question Bank with Answers
PART- A
Q.No Questions Marks CO BL
What is the need for probability theory in uncertainty ?
Probability provides the way of summarizing the uncertainty that
1 comes from our laziness and ignorance . 2 CO4 R
Probability statements do not have quite the same kind of
semantics known as evidences
What is called as principle of maximum expected utility ?
The basic idea is that an agent is rational if and only if it chooses
2
2 the action that yields the highest expected utility, averaged over all CO4 U
the possible outcomes of the action.
This is known as MEU.
What Is Called As Decision Theory ?
Preferences As Expressed by Utilities Are Combined with
2
3 Probabilities in the General Theory of Rational Decisions Called Decision CO4 U
Theory.
Decision Theory = Probability Theory + Utility Theory.
What is the purpose of learning?
2
4 The idea behind learning is that percepts should be used not only for CO4 R
acting but also for improving the agent’s ability to act in the future.
Define conditional probability?
Once the agents has obtained some evidence concerning the
previously unknown propositions making up the domain
2
5 conditional or posterior probabilities with the notation p(A/B) is CO2 U
used.
This is important that p(A/B) can only be used when all be is
known.
Give the Baye's rule equation.
W.K.T P(A ^ B) = P(A/B) P(B) -------------------------- 1
P(A ^ B) = P(B/A) P(A) -------------------------- 2 2
6 CO3 R
DIVIDING BY P(A) ;
WE GET
P(B/A) = P(A/B) P(B) -------------------- P(A)
Differentiate Supervised and Unsupervised learning.
2
7 CO3 U
, Define Reinforcement Learning.
This Learning is rather than being told what to do by teacher, a
reinforcement learning agent must learn from occasional rewards. 2
8 CO3 R
Example
If taxi driver does not get a tip at the end of journey, it gives him a
indication that his behaviour is undesirable.
What is training set?
The complete set of examples is called the training set.
2
9 Example CO3 U
Restaurant problem
Goal predicate “will wait”
Define Information gain.
Information gain from the attribute test is the difference between
2
10 the original information requirement and the new requirement. CO3 R
Gain (A) = I(p/(p+n)), n/ (p+n)) – Remainder(A)
What is test set?
Prediction is good if it turns out to be true, so can assess quality of
hypotheses by 2
11 CO3 U
Checking its predictions against the correct classification once we
know it. We do this on a set of examples is known as Test Set.
Mention the exercises which broaden the applications of decision
trees.
i. Missing data 2
12 CO3 R
ii. Multivalued attributes
iii. Continuous and integer valued input attributes
iv. Continuous valued output attributes
Define Bayesian Learning.
It calculates the probability of each hypotheses, given the data and
makes predictions on that basis, 2
13 CO2 U
(i.e.) predictions are made by using all the hypotheses, weighted
by their probabilities rather than by using just single “best”
hypotheses.
Define Neural Networks.
It consists of nodes or units connected by directed links. A link
2
14 propagates the activation. CO2 U
Each link has a numeric weight which determines the strength and
sign of the connection.
What is called as Markov Decision problem?
The problem of calculating an optimal policy in an accessible, 2
15 CO2 R
stochastic environment with a known transition model is called a
Markov Decision Problem(MDP)
, What are the two functions in neural networks activation function?
(i) Threshold function 2
16 CO2 R
(ii) Sigmoid function
What is over fitting?
Whenever there is a large set of possible hypotheses, one has to be careful 2
17 CO2 A
not to use the resulting freedom to find meaningless “regularity” in the
data. This problem is called over fitting.
What is parity and majority function?
Parity Function : It Returns 1 if and only if an even number of inputs are 2
18 CO3 R
1.Majority function : It Returns 1 if more than half of its inputs are 1.
Define Regression learning. 2
19 CO2 R
Learning a continuous valued function is called regression learning.
Define Classification Learning.
Learning a discrete valued function is called is called classification
learning. 2
20 CO3 U
PART- B
Q.No Questions Marks CO BL
1 Establish Bayesian networks for a specific application. (16 marks) CO4 AP
Bayesian networks are probabilistic, because these networks are built
from a probability distribution, and also use probability theory for prediction
and anomaly detection.
Directed Acyclic Graph (3 marks)
A Bayesian network graph is made up of nodes and Arcs (directed links),
o Each node corresponds to the random variables, and a variable can
be continuous or discrete.
o Arc or directed arrows represent the causal relationship or conditional
probabilities between random variables. These directed links or arrows
connect the pair of nodes in the graph.
These links represent that one node directly influence the other node, and
if there is no directed link that means that nodes are independent with
each other
o In the above diagram, A, B, C, and D are random variables
represented by the nodes of the network graph.
o If we are considering node B, which is connected with node A
by a directed arrow, then node A is called the parent of Node
(Autonomous)
B.Tech – Information Technology
IV SEMESTER
20IT402 COMPUTATIONAL INTELLIGENCE
Regulations 2020
Question Bank with Answers
PART- A
Q.No Questions Marks CO BL
What is the need for probability theory in uncertainty ?
Probability provides the way of summarizing the uncertainty that
1 comes from our laziness and ignorance . 2 CO4 R
Probability statements do not have quite the same kind of
semantics known as evidences
What is called as principle of maximum expected utility ?
The basic idea is that an agent is rational if and only if it chooses
2
2 the action that yields the highest expected utility, averaged over all CO4 U
the possible outcomes of the action.
This is known as MEU.
What Is Called As Decision Theory ?
Preferences As Expressed by Utilities Are Combined with
2
3 Probabilities in the General Theory of Rational Decisions Called Decision CO4 U
Theory.
Decision Theory = Probability Theory + Utility Theory.
What is the purpose of learning?
2
4 The idea behind learning is that percepts should be used not only for CO4 R
acting but also for improving the agent’s ability to act in the future.
Define conditional probability?
Once the agents has obtained some evidence concerning the
previously unknown propositions making up the domain
2
5 conditional or posterior probabilities with the notation p(A/B) is CO2 U
used.
This is important that p(A/B) can only be used when all be is
known.
Give the Baye's rule equation.
W.K.T P(A ^ B) = P(A/B) P(B) -------------------------- 1
P(A ^ B) = P(B/A) P(A) -------------------------- 2 2
6 CO3 R
DIVIDING BY P(A) ;
WE GET
P(B/A) = P(A/B) P(B) -------------------- P(A)
Differentiate Supervised and Unsupervised learning.
2
7 CO3 U
, Define Reinforcement Learning.
This Learning is rather than being told what to do by teacher, a
reinforcement learning agent must learn from occasional rewards. 2
8 CO3 R
Example
If taxi driver does not get a tip at the end of journey, it gives him a
indication that his behaviour is undesirable.
What is training set?
The complete set of examples is called the training set.
2
9 Example CO3 U
Restaurant problem
Goal predicate “will wait”
Define Information gain.
Information gain from the attribute test is the difference between
2
10 the original information requirement and the new requirement. CO3 R
Gain (A) = I(p/(p+n)), n/ (p+n)) – Remainder(A)
What is test set?
Prediction is good if it turns out to be true, so can assess quality of
hypotheses by 2
11 CO3 U
Checking its predictions against the correct classification once we
know it. We do this on a set of examples is known as Test Set.
Mention the exercises which broaden the applications of decision
trees.
i. Missing data 2
12 CO3 R
ii. Multivalued attributes
iii. Continuous and integer valued input attributes
iv. Continuous valued output attributes
Define Bayesian Learning.
It calculates the probability of each hypotheses, given the data and
makes predictions on that basis, 2
13 CO2 U
(i.e.) predictions are made by using all the hypotheses, weighted
by their probabilities rather than by using just single “best”
hypotheses.
Define Neural Networks.
It consists of nodes or units connected by directed links. A link
2
14 propagates the activation. CO2 U
Each link has a numeric weight which determines the strength and
sign of the connection.
What is called as Markov Decision problem?
The problem of calculating an optimal policy in an accessible, 2
15 CO2 R
stochastic environment with a known transition model is called a
Markov Decision Problem(MDP)
, What are the two functions in neural networks activation function?
(i) Threshold function 2
16 CO2 R
(ii) Sigmoid function
What is over fitting?
Whenever there is a large set of possible hypotheses, one has to be careful 2
17 CO2 A
not to use the resulting freedom to find meaningless “regularity” in the
data. This problem is called over fitting.
What is parity and majority function?
Parity Function : It Returns 1 if and only if an even number of inputs are 2
18 CO3 R
1.Majority function : It Returns 1 if more than half of its inputs are 1.
Define Regression learning. 2
19 CO2 R
Learning a continuous valued function is called regression learning.
Define Classification Learning.
Learning a discrete valued function is called is called classification
learning. 2
20 CO3 U
PART- B
Q.No Questions Marks CO BL
1 Establish Bayesian networks for a specific application. (16 marks) CO4 AP
Bayesian networks are probabilistic, because these networks are built
from a probability distribution, and also use probability theory for prediction
and anomaly detection.
Directed Acyclic Graph (3 marks)
A Bayesian network graph is made up of nodes and Arcs (directed links),
o Each node corresponds to the random variables, and a variable can
be continuous or discrete.
o Arc or directed arrows represent the causal relationship or conditional
probabilities between random variables. These directed links or arrows
connect the pair of nodes in the graph.
These links represent that one node directly influence the other node, and
if there is no directed link that means that nodes are independent with
each other
o In the above diagram, A, B, C, and D are random variables
represented by the nodes of the network graph.
o If we are considering node B, which is connected with node A
by a directed arrow, then node A is called the parent of Node
