STUPIDSID
Solve any one question from Q 1(a,b) & Q 2. (a, b)
1(a)
write mathematical form of the following:
i) Classitication.
ii) Class probability estimation. iii)Regression.
Which one out of these three is more precise? Which one leads to overfitting?
i) Classitication.
ii) Class probability estimation. iii)Regression.
Which one out of these three is more precise? Which one leads to overfitting?
4 M
1(b)
Prove with an example FP=Neg-TN.
4 M
2(a)
Write output code matrix for one-versus-one symmetric case. Assume three classes.
4 M
2(b)
Justify use of Machine Learning to solve following task:?Prediction of sale value of a car based on the locality of the property?.
4 M
Solve any one question from Q 3(a,b) & Q 4. (a, b)
3(a)
Explain VC dimention.
4 M
3(b)
Explain Kernel methods for non-linearity.
4 M
4(a)
What is Machine Learning? Explain any one application where Machine Learning can be used.
4 M
4(b)
Explain Support Vector Machine.
4 M
Solve any one question from Q 5(a,b) & Q 6. (a, b)
5(a)
Final all 3- item itemsets from this set with minimum support=2.
| Trans_id | Itemlist |
| T1 | {K, A, D, B} |
| T2 | {D, A, C, E, B} |
| T3 | {C, A, B, E} |
| T4 | {B, A, D} |
9 M
5(b)
Write K-means Algorithm.
9 M
6(a)
Explain silhouettes.
9 M
6(b)
Discuss various distance measures.
9 M
Solve any one question from Q 7(a,b) & Q 8. (a, b)
7(a)
Write a note on compression based models.
8 M
7(b)
Explain Naive Bayes Classification Algorithm
8 M
8(a)
Difine the terms:
i) Bernoulli distribution.
ii) Binomial distribution
iii) Multinomial distribution
iv) Gaussion distribution.
i) Bernoulli distribution.
ii) Binomial distribution
iii) Multinomial distribution
iv) Gaussion distribution.
8 M
8(b)
Explain Discriminative learning.
8 M
Solve any one question from Q 9(a,b) & Q 10. (a, b)
9(a)
Explain on-line learning.
8 M
9(b)
Explain multi task learning.
8 M
10(a)
Explain the concept of penalty and award in reinforcement learning.
8 M
10(b)
Explain ensemble learning.
8 M
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