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Machine Learning Question Paper - Dec 18 - Computer Engineering (Semester 8) - Mumbai University (MU)
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Machine Learning - Dec 18

Computer Engineering (Semester 8)

Total marks: 80
Total time: 3 Hours
INSTRUCTIONS
(1) Question 1 is compulsory.
(2) Attempt any three from the remaining questions.
(3) Draw neat diagrams wherever necessary.

1

a) Define well posed learning problem. Hence, define robot driving learning problem.
(5 marks) 00

b) Explain, in brief, Bayesian Beleif networks.
(5 marks) 00

c) Write short note on Temporal Difference Learning.
(5 marks) 00

d) Explain procedure to construct decision trees.
(5 marks) 00

2

a) Explain how support vector machine can be used to find optimal hyperplane to classify lineraly separable data. Give suitable examples.
(10 marks) 00

b) Explain procedure to design machine learning procedure.
(10 marks) 00

3

a) What is linear regression? Find the best fitted line for following example:
(10 marks) 00

i $x_i$ $y_i$ ($y^2)_i$
1 63 127 120.1
2 64 121 126.3
3 66 142 138.5
4 69 157 157.0
5 69 162 157.0
6 71 156 169.2
7 71 169 169.2
8 72 165 175.4
9 73 181 181.5
10 75 208 193.8

b) What is decision tree? How you will choose beat attribute for decision tree classifier? Give suitable example.
(10 marks) 00

4

a) Explain K-mean clustering algorithm giving suitable example. Also, explain how k-mean clustering differs from heirarchical clustering.
(10 marks) 00

b) What is kernel? How kernel can be used with SVM to classify non-lineraly seperable data? Also, list standard kernel functions.
(10 marks) 00

5

a) What is Q-learning? Explain algorithm for learning Q.
(10 marks) 00

b) Explain following terms with respect to Reinforcement learning.: delayed rewards, exploration, and partially observable states.
(10 marks) 00

6

a) Soft margin SVM
(5 marks) 00

b) Radial Basis functions
(5 marks) 00

c) Independent Component Analysis
(5 marks) 00

d) Logistic Regression
(5 marks) 00

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