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Machine Learning Question Paper - May 17 - Computer Engineering (Semester 8) - Mumbai University (MU)
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Machine Learning - May 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)
What is machine learning? Explain how supervised learning is difficult from unsupervised learning.
(5 marks)
00
B)
Explain Bayes theorem.
(5 marks)
00
C)
What are the elements of reinforcement learning?
(5 marks)
00
D)
Describe the two methods for reducing dimensionality
(5 marks)
00
Q2
A)
The following table shows the midterm and final exam grade obtained for students in a database course.
(10 marks)
00
| Midterm exam(x) | Final exam (y) |
|---|---|
| 72 | 84 |
| 50 | 63 |
| 81 | 77 |
| 74 | 78 |
| 94 | 90 |
| 86 | 75 |
| 59 | 49 |
| 83 | 79 |
| 65 | 77 |
| 33 | 52 |
| 88 | 74 |
| 81 | 90 |
Use the method of least squares using regression to predict the final exam grade of a student who received 86 on the midterm exam.
B
Expalin the steps in developing a machine learning application.
(10 marks)
00
Q3
A
For a SunBurn dataset given below, construct a decision tree.
(10 marks)
00
| Name | Hair | Height | Weight | Location | Class |
|---|---|---|---|---|---|
| Sunita | Blonde | Average | Light | No | Yes |
| Anita | Blonde | Tall | Average | Yes | No |
| Kavita | Brown | Short | Average | Yes | No |
| Sushma | Blonde | Short | avarage | no | yes |
| Xavier | red | average | heavy | no | yes |
| Balaji | brown | tall | heavy | no | no |
| Ramesh | brown | average | heavy | no | no |
| Swetha | blonde | short | light | yes | no |
B
What is Support Vector Machine(SVM)? How to compute the margin?
(10 marks)
00
Q4
A
Explain Hidden Markov Models.
(10 marks)
00
B
Use Principle Component analysis (PCA) to arrive at the transformed matix for the given matrix A.
(10 marks)
00
$A^T=$
$ \begin{bmatrix}
2 & 1 & 0 & 1 \\
4 & 3 & 1 & 0.5
\end{bmatrix} $
Q5
A
Explain how Back Propagation algorithm helps in classification
(10 marks)
00
B
For the given set of points identify clusters using complete link and average link using agglomerative clustering.
(10 marks)
00
| - | A | B |
|---|---|---|
| P1 | 1 | 1 |
| P2 | 1.5 | 1.5 |
| P3 | 5 | 5 |
| P4 | 3 | 4 |
| P5 | 4 | 4 |
| P6 | 3 | 3.5 |
Q6 Write short note on any two:
A
Temporal difference learning
(5 marks)
00
B
Logistic regression
(5 marks)
00
C
Machine learning Applications
(5 marks)
00
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