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Subject Code: KCS055
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B.TECH.
(SEM V) THEORY EXAMINATION 2021-22
MACHINE LEARNING TECHNIQUES
Time: 3 Hours
Total Marks: 100
Note: 1. Attempt all Sections. If require any missing data; then choose suitably.
SECTION A
2 x 10 = 20
Qno.
a.
b.
c.
d.
e.
f.
g.
h.
i.
j.
Question
What is a “Well -posed Learning “problem? Explain with an example.
What is Occam's razor in ML?
What is the role of Inductive Bias in ANN?
What is gradient descent delta rule?
What is Paired t Tests in Hypothesis evaluation?
How do you find the confidence interval for a hypothesis test?
What is sample complexity of a Learning Problem?
Differentiate between Lazy and Eager Learning
What is the problem of crowding in GA
Comparison of purely analytical and purely inductive learning.
Marks
2
2
2
2
2
2
2
2
2
2
9.
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Attempt all questions in brief.
29
1.
Attempt any three of the following:
Qno.
a.
b.
c.
d.
e.
Question
Design the Final design of checkers learning program.
What is Maximum Likelihood and Least Squared Error Hypothesis?
What problem does the EM algorithm solve
Highlight the importance of Case Based Learning
Write short notes on Learning First Order Rules
10
10
10
10
10
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SECTION C
Marks
09
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24
SECTION B
Attempt any one part of the following:
Qno.
a.
b.
Question
Explain the “Concept Learning” Task Giving an example
Find the maximally general hypothesis and maximally specific hypothesis
for the training examples given in the table using the candidate elimination
algorithm.
Marks
10
10
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3.
Given Training Example:
Sky
Temp
Humidity
wind
water
Forecast
sport
Sunny
warm
Normal
Strong
warm
same
Yes
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QP22O1P_029 | 10-Jan-2022 09:00:30 | 125.21.249.98
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Subject Code: KCS055
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Sunny
warm
High
Strong
warm
same
Yes
Rainy
cold
High
Strong
warm
change
No
Sunny
warm
High
Strong
cool
change
Yes
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4.
Attempt any one part of the following:
Qno.
a.
Question
Marks
Comment on the Algorithmic convergence & Generalization property of 10
ANN.
Discuss the following issues in Decision Tree Learning:
10
1. Overfitting the data
2. Guarding against bad attribute choices
3. Handling continuous valued attributes
4. Handling missing attribute values
5. Handling attributes with differing costs
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98
29
b.
0
Attempt any one part of the following:
Qno.
a.
b.
Question
Marks
How is Naïve Bayesian Classifier different from Bayesian Classifier?
10
Explain the role of Central Limit Theorem Approach for deriving 10
Confidence Interval.
6.
Attempt any one part of the following:
Qno.
a.
b.
Question
Marks
Write short notes on Probably Approximately Correct (PAC) learning 10
model.
Discuss various Mistake Bound Model of Learning
10
7.
Attempt any one part of the following:
Qno.
a.
b.
Question
Marks
What is the significance of Learn -one Rule Algorithm?
10
Describe a prototypical genetic algorithm along with various operations 10
possible in it.
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