Download JNTUH MCA 4th Sem R17 2019 December 844AC Machine Learning Question Paper

FirstRanker.com
Firstranker's choice

Code No: 844AC

--- Content provided by‍ FirstRanker.com ---

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

MCA IV Semester Examinations, December - 2019

MACHINE LEARNING

Time: 3hrs Max.Marks:75

Note: This question paper contains two parts A and B.

--- Content provided by​ FirstRanker.com ---

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

1. a) List and explain the issues in machine learning. [5]

b) Discuss in brief about neural network representation. [5]

c) Describe briefly about Bayesian Belief Networks. [5]

--- Content provided by​ FirstRanker.com ---

d) What are temporal patterns? Explain. [5]

e) Briefly discuss about explanation based learning. [5]

PART -B 5 x 10 Marks =50

2. In the following, it is desired to describe whether a person is ill. We use a representation based on conjunctive constraints (three per subject) to describe individual person. These constraints are “running nose”, “coughing”, and “reddened skin”, each of which can take the value true (“+") or false (‘-"). We say that somebody is ill, if he is coughing and has a running nose. Each single symptom individually does not mean that the person is ill. Specify the space of hypotheses that is being managed by the version space approach. Arrange all hypotheses in a graph structure using the more-specific-than relation. [10]

OR

--- Content provided by⁠ FirstRanker.com ---

3. Table below shows the relationship between the body height and the gender of a group of persons (the records have been sorted with respect to the value of height in cm). Calculate the information gain for potential splitting thresholds and determine the best one. [10]

Height | 161 164 169 175 176 179 180 184 185

Gender | F F M M F F M M F

4. Describe how the basic Back-Propagation Learning Algorithm is used in Multi-Layer Network. [10]

OR

--- Content provided by‍ FirstRanker.com ---

5. Consider a learned hypothesis, h, for some boolean concept. When h is tested on a set of 100 examples, it classifies 83 correctly. What is the standard deviation and the 95% confidence interval for the true error rate for Errorp(h)? [10]

6. Describe in detail about Maximum Likelihood Hypotheses for Predicting probabilities. [10]

OR

7. Give an example to explain the concept of K-Nearest neighbor algorithm. [10]

8. Explain in detail about Dynamic Time Warping Methods. [10]

--- Content provided by‌ FirstRanker.com ---

OR

9. Explain how training and testing is performed in discrete hidden markov models. [10]

10. Describe how prior knowledge is used to alter the Search Objective. [10]

OR

11. Illustrate how learning can be performed using inductive-analytical approach. [10]

--- Content provided by‌ FirstRanker.com ---

FirstRanker.com