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

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

MCA IV Semester Examinations, April/May - 2019

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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 x 5 Marks =25

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1. a) Define Inductive bias. [5]
2. b) Define: 1) Sample error ii) True error [5]
3. c) Explain minimum description length principle. [5]
4. d) Explain briefly codebook generation. [5]
5. e) Explain using prior knowledge to alter the search objective. [5]

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PART -B 5 x 10 Marks =50

1. 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
2. Write the Candidate-Elimination Algorithm. [10]
3. 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]
   

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   OR
4. a) Explain basics of sampling theory.
   b) Explain Error estimation and estimating Binomial Proportions. [5+5]
5. Explain the working of Naive Bayes classifier with necessary formulae and with an example. [10]
   OR

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6. a) Explain the working of Bayes optimal classifier with an example.
   b) Explain Maximum Description Length principle. [5+5]
7. Explain Discrete Markov Processes. [10]
   OR
8. Explain the working of HMMs. [10]

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9. a) Explain Inductive and analytical learning problems with examples.
   b) Write the explanation based learning algorithm: Prolog-EBG. [5+5]
   OR
10. Write and explain KBAINN algorithm. [10]

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