# Download JNTUH MCA 4th Sem R17 2019 April-May 844AC Aprilmay Machine Learning Question Paper

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

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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