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Machine Learning Question Paper - Dec 17 - Computer Engineering (Semester 8) - Mumbai University (MU)
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Machine Learning - Dec 17
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.
Q1
a)
Explain the key terminologies of Support Vector Machine.
(5 marks)
00
b)
What are the keys tasks of Machine Learning?
(5 marks)
00
c)
Explain the concepts behind Linear Regression.
(5 marks)
00
d)
Explain in brief elements of Reinforcement Learning.
(5 marks)
00
Q2
a)
Explain the steps required for selecting the right machine learning algorithm.
(8 marks)
00
b)
For the given determine the entropy after classification using each attribute for classification saparetly and find which attribute is best as decision attribute for the root by finding information gain with respect to entropy of Temprature as refrence attribute.
(12 marks)
00
| Sr No. | Temperature | Wind | Humidity |
|---|---|---|---|
| 1 | hot | weak | normal |
| 2 | hot | strong | high |
| 3 | mild | weak | normal |
| 4 | mild | strong | high |
| 5 | cool | weak | normal |
| 6 | mild | strong | normal |
| 7 | mild | weak | high |
| 8 | hot | strong | normal |
| 9 | mild | strong | normal |
| 10 | cool | strong | normal |
Q3
a)
Apply k-means algorithm on given data for k=2. Use $C_1$(2,4) & $C_2$(6,3) as intial cluster centres.
(10 marks)
00
Data : a(2,4),b(3,3),c(5,5),d(6,3),e(4,3),f(6,6)
b)
Explain classification using Bayesian Beleif Network with an example.
(10 marks)
00
Q4
a)
Define support vector machine(SVM) and further explain the maximum margin linear separators concept.
(10 marks)
00
b)
Explain in detail Principal Component Analysis for dimension reduction
(10 marks)
00
Q5
a)
Explain reinforcement learning in detail along with the various elements involved in forming the concept. Also define what is meant by partially observable state.
(10 marks)
00
b)
Apply Agglomerative clustering algorithm on given data and draw dendogram. Show three clusters with its allocated points. Use single link method.
(10 marks)
00
Adjacency Matrix:
| a | b | c | d | e | f | |
|---|---|---|---|---|---|---|
| a | 0 | $2^{\frac{1}{2}}$ | $10^{\frac{1}{2}}$ | $17^{\frac{1}{2}}$ | $5^{\frac{1}{2}}$ | $20^{\frac{1}{2}}$ |
| b | $2^{\frac{1}{2}}$ | 0 | $8^{\frac{1}{2}}$ | 3 | 1 | $18^{\frac{1}{2}}$ |
| c | $10^{\frac{1}{2}}$ | $8^{\frac{1}{2}}$ | 0 | $5^{\frac{1}{2}}$ | 1 | 1 |
| d | $17^{\frac{1}{2}}$ | 3 | $5^{\frac{1}{2}}$ | 0 | 2 | 3 |
| e | $5^{\frac{1}{2}}$ | 1 | 1 | 2 | 0 | $13^{\frac{1}{2}}$ |
| f | $20^{\frac{1}{2}}$ | $18^{\frac{1}{2}}$ | 1 | 3 | $13^{\frac{1}{2}}$ | 0 |
Q6 Write detail notes on any two:
a)
Hierarchical Clustering algorithms
(10 marks)
00
b)
Hidden Markov model
(10 marks)
00
c)
Model Based Learning
(10 marks)
00
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