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