0520MCA201122101 ९५४

നി ١ Module 111 ।
‏ین‎ Consider the following set of training examples: ۷

Classification | at |
( | * |
3

[1 |) |

| - ۶

اتا

| _

॥ - |
‏ہم‎

Find the entropy of this collection of training examples with respect to the target

function “classification”? (3 marks)
९ ८८५५० the information gain of a2 relative to these training examples? (3

7

ب

marks)
OR
१४ - How to estimate the parameters of a linear regression model ?
Module IV
[ _ Discuss the basic idea behind the back propagation algorithm.
OR

##स्‍- Define linearly separable dataset. Give an example each of a dataset that is
linearly separable and of a dataset that is not linearly separable.

(3 marks)
டிவி kernel function. Explain the kernel trick to construct a classifier for a

dataset that is not linearly separable. (3 marks)
Module V

19 Suppose 10000 patients get tested for flu; out of them, 9000 are actually healthy
and 1000 are actually sick. For the sick people, a test was positive for 620 and

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