Download JNTUH (Jawaharlal nehru technological university) MCA (Master of Computer Applications) 4th Sem (Fourth Semester) Regulation-R17 2019 April-May 844AC Aprilmay Machine Learning Previous Question Paper
R17
Code No: 844AC
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
MCA IV Semester Examinations, April/May - 2019
MACHINE LEARNING
Time: 3hrs
Max.Marks:75
Note: This question paper contains two parts A and B.
Part A is compulsory which carries 25 marks. Answer all questions in Part A. Part B
consists of 5 Units. Answer any one full question from each unit. Each question carries
10 marks and may have a, b, c as sub questions.
PART - A
5 ? 5 Marks = 25
1.a)
Define Inductive bias.
[5]
b)
Define: i) Sample error ii) True error
[5]
c)
Explain minimum description length principle.
[5]
d)
Explain briefly codebook generation.
[5]
e)
Explain using prior knowledge to alter the search objective.
[5]
PART - B
5 ? 10 Marks = 50
2.
Hand trace the Candidate ? Elimination algorithm on the following training data.
s.no Sky
Air Temp Humidity Wind
Water Forecast Enjoy sport
1
Sunny warm
Normal
light
warm same
yes
2
Sunny Warm
High
strong cool
change
yes
3
Rainy Cold
High
Strong Warm Change No
4
Sunny Warm
High
Strong Warm Same
Yes
5
Sunny Warm
Normal
Strong Warm Same
yes
You should hand trace the algorithm by performing the tracing with examples given in
the table in the ascending order of serial number.
[10]
OR
3.
Write the Candidate-Elimination Algorithm.
[10]
4.
Write the relevant Mathematical formulae and describe the working of Perceptron with
a neat diagram. Hand trace the perceptron learning rule to implement 2 input EX-OR
gate for 2 iterations through all 4 training examples.
[10]
OR
5.a)
Explain basics of sampling theory.
b)
Explain Error estimation and estimating Binomial Proportions.
[5+5]
6.
Explain the working of Na?ve Bayes classifier with necessary formulae and with an
example.
[10]
OR
7.a) Explain the working of Bayes optimal classifier with an example.
b) Explain Maximum Description Length principle.
[5+5]
8.
Explain Discrete Markov Processes.
[10]
OR
9.
Explain the working of HMMs.
[10]
10.a) Explain Inductive and analytical learning problems with examples.
b) Write the explanation based learning algorithm: Prolog-EBG.
[5+5]
OR
11. Write and explain KBANN algorithm.
[10]
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This post was last modified on 17 March 2023