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Model Question Paper-1 with effect from 2019-20 (CBCS Scheme)
USN
Sixth Semester B.E. Degree Examination
Machine Learning
TIME: 03 Hours Max. Marks: 100
Note: 01. Answer any FIVE full questions, choosing at least ONE question from each MODULE.
Module – 1
(a) What is Machine Leaning? Explain the applications of Machine Learning. 04M
Q.1
(b) Discuss the any four main challenges of machine learning 08M
Consider the “Japanese Economy Car” concept and instance given in Table 1., Illustrate
(c) 08M
the hypothesis using Candidate Elimination Learning algorithm.
Table 1.
Origin Manufacturer Color Decade Type Example Type
Japan Honda Blue 1980 Economy Positive
Japan Toyota Green 1970 Sports Negative
Japan Toyota Blue 1990 Economy Positive
USA Chrysler Red 1980 Economy Negative
Japan Honda White 1980 Economy Positive
OR
Explain Find-S algorithm ad show its working by taking the enjoy sport concept and
(a) training instances given in Table 2. 10M
Enjoy
Q.2 Example Sky AirTemp Humidity Wind Water Forecast Sport
1 Sunny Warm Normal Strong Warm Same Yes
2 Sunny Warm High Strong Warm Same Yes
3 Rainy Cold High Strong Warm Change No
4 Sunny Warm High Strong Cool Change Yes
Table 2.
Discuss the features of an unbiased Learner.
(b) 06M
State the following problems with respect to Tasks, Performance, and Experience:
(c) 04M
i)A Checkers learning problem ii) A Robot driving learning problem.
Module – 2
(a) In context to prepare the data for Machine Learning algorithms, Write a note on 10M
Q.3 (i) Data Cleaning (ii) Handling text and categorical attributes iii)Feature scaling
(b) With the code snippets show how Grid Search and Randomized Search helps in Fine- 10M
Tuning a model.
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OR
(a) Using code snippets, outline the concepts involved in 10M
i) Measuring accuracy using Cross-Validation.
ii) Confusion Matrix.
Q.4 iii) Precision and Recall.
(b) With the code snippet explain how Multilabels classification different from multiclass 10M
Multioutput classification?
Module – 3
Q.5 (a) what is gradient descent algorithm and discuss its various types. 10M
In Regularized Linear Models illustrate the three different methods to constrain the 10M
(b) weights.
OR
(a) With respect to Nonlinear SVM Classification, explain Polynomial Kernel Gaussian 10M
and RBF Kernel along with code snippet.
Q.6 (b) Show that how SVMs make predictions using Quadratic Programming and Kernelized 10 M
SVM.
Module – 4
(a) With an example dataset examine how Decision Trees are used in making predictions. 10M
Q.7
(b) Explain The CART Training Algorithm. 06M
(c) Identify the features of Regression and Instability w.r.t decision trees. 04M
OR
(a) In context to Ensemble methods determine the concepts of 10M
i) Bagging and Pasting.
Q.8 ii) Voting Classifiers.
(b) Examine the following boosting methods along with code snippets. 10M
i) AdaBoost
ii) Gradient Boosting
Module – 5
(a) Write Bayes theorem. Identify the relationship between Bayes theorem and the 10M
problem of concept learning?
Show that how Maximum Likelihood Hypothesis is helpful for predicting 10M
Q.9 (b) probabilities.
OR
(a) Construct Naïve Bayes Classifier with an Example. 10M
(b) Derive the EM Algorithm in detail. 10M
Q.10
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Table showing the Bloom’s Taxonomy Level, Course Outcome and Programme
Outcome
Question Bloom’s Taxonomy Level Course Programme Outcome
attached Outcome
Q.1 (a) L1 CO1 PO1
(b) L1 CO1 PO1
(c) L2 CO1 PO1
Q.2 (a) L1 CO1 PO2
(b) L2 CO1 PO2
(c) L1 CO1 PO2
Q.3 (a) L2 CO1 PO3
(b) L2 CO1 PO3
Q.4 (a) L2 CO1 PO3
(b) L2 CO1 PO3
Q.5 (a) L2 CO2 PO3
(b) L2 CO2 PO4
Q.6 (a) L1 CO2 PO5
(b) L2 CO2 PO6
Q.7 (a) L4 CO2 PO9
(b) L2 CO2 PO12
(c) L3 CO2 PO5
Q.8 (a) L3 CO2 PO6
(b) L4 CO2 PO9
Q.9 (a) L3 CO3 PO9
(b) L3 CO3 PO4
Q.10 (a) L3 CO3 PO5
(b) L3 CO3 PO12
Lower order thinking skills
Bloom’s Remembering( Understanding Applying (Application):
Taxonomy knowledge):𝐿1 Comprehension): 𝐿2 𝐿3
Levels Higher order thinking skills
Analyzing (Analysis): 𝐿4 Valuating (Evaluation): 𝐿5 Creating (Synthesis): 𝐿6
