Download JNTUH (Jawaharlal nehru technological university) MCA (Master of Computer Applications) 4th Sem (Fourth Semester) Regulation-R17 2019 December 844AC Machine Learning Previous Question Paper
R17
Code No: 844AC
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
MCA IV Semester Examinations, December - 2019
MACHINE LEARNING
Time: 3hrs
Max.Marks:75
S
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) 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]
d)
What are temporal patterns? Explain.
[5]
e) Briefly discuss about explanation based learning.
[5]
PART - B
5 ? 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
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
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 ErrorD(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]
OR
9.
Explain how training and testing is performed in discrete hidden markov models. [10]
S
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]
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This post was last modified on 17 March 2023