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MU Computer Engineering (Semester 8)
Machine Learning
May 2016
Total marks: --
Total time: --
INSTRUCTIONS
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

1(a) What are the key tasks of Machine Learning?
5 M
1(b) What are the key terminologies of Support Vector Machine?
5 M
1(c) Explain in brief Linear Regression Technique.
5 M
1(d) Explain in brief elements of Reinforcement Learning.
5 M

2(a) Explain the steps required for selecting the right machine learning algorithm.
8 M
2(b) For the given data determine the entropy after classification using each attribute for classification separately and find which attribute is best as decision attribute for the root by finding information gain with respect to entropy of Temperature as reference attribute.
 Sr.No Temperature Wind Humidity 1 Hot Weak High 2 Hot Strong High 3 Mild Weak Normal 4 Cool Strong High 5 Cool Weak Normal 6 Mild Strong Normal 7 Mild Weak High 8 Hot Strong High 9 Mild Weak Normal 10 Hot Strong
12 M

3(a) Explain in detail Principal Component Analysis for Dimension Reduction
10 M
3(b) Apply K-means algorithm on given data for k=3. Use C1(2) , C2(16) and C3(38) as initial cluster centres.
Data: 2, 4, 6, 3, 31, 12, 15, 16, 38, 35, 14, 21, 23, 25, 30
10 M

4(a) Explain in detail reinforcement technique Temporal Difference Learning.
10 M
4(b) Using Bayesian classification and the given data classify the tuple(Rupesh, M, 1.73 m)
 Attribute Value Count Probability Short Medium Tall Short Medium Tall Gender M 1 2 3 1/4 2/7 3/4 F 3 5 1 3/4 5/7 1/4 Height (o,16) 2 0 0 2/4 0 0 (1.6, 1.7) 2 0 0 2/4 0 0 (1.7, 1.8) 0 3 0 0 3/7 0 (1.8, 1.9) 0 3 0 0 3/7 0 (1.9, 2) 0 1 2 0 1/7 2/4 (2, ∞) 0 0 2 0 0 2/4
10 M

5(a) Apply Agglomerative clustering algorithm on given data and draw dendogram. Show three clusters with its allocated points. Use single link method.
 Adjacency Matrix a b c d e f a 0 √2 √10 √17 √5 √20 b √2 0 √8 3 1 √18 c √10 √8 0 √5 √5 2 d √17 3 √5 0 2 3 e √5 1 √5 2 0 √13 f √20 √18 2 3 √13 0
8 M
5(b) Explain classification using Back Propagation algorithm with a suitable example.
12 M

Write detail notes on (Any two)
6(a) Quadratic Programming solution for finding maximum margin separation in Support Vector Machine.
10 M
6(b) Application of Machine Learning algorithms.
10 M
6(c) Hidden Markov Model
10 M

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