Solve any one question from Q.1(a,b) & Q.2(a,b)

1(a)
Explain logical models. State examples.

5 M

1(b)
What is a perceptron? Explain with the help of an example.

5 M

2(a)
With an example, explain feature as a split and feature as a predictor.

5 M

2(b)
Calculate accuracy, precision and recall for the following:

Predicted + | Predicted - | |

Actual + | 60 | 15 |

Actual - | 10 | 15 |

5 M

Solve any one question from Q.3(a,b) & Q.4(a,b)

3(a)
When is it suitable to use linear regression over classification?

5 M

3(b)
State formulate for calculating accuracy, true positive rate, true negative rate, false positive rate and false negative rate for binary classification tasks.

5 M

4(a)
Explain training dataset, test dataset and supervised learning.

5 M

4(b)
Why do we need to regularize in regression? Explain.

5 M

Solve any one question from Q.5(a,b) & Q.6(a,b)

5(a)
Explain four distance function. Name any machine learning task which uses distance functions.

9 M

5(b)
Write note on clustering trees.

9 M

6(a)
Write a note on subgroup discovery.

9 M

6(b)
Explain single linkage, complete linkage and average linkage.

9 M

Solve any one question from Q.7(a,b) & Q.8(a,b)

7(a)
Is Naive Bayes algorithm supervised or unsupervised task? Explain how it achieves the task you specified.

8 M

7(b)
Write a note on normal distribution.

8 M

8(a)
What is multivariate Bernoulli distribution?

8 M

8(b)
Using the following data, find 2-item-itemsets which have minimum support =2.

Transaction | Items |

1 | nappies |

2 | beer, crisps |

3 | apples, nappies |

4 | beer, crisps, nappies |

5 | apples |

6 | apples, beer, crisps, nappies |

7 | apples, crisps |

8 | crisps |

8 M

Solve any one question from Q.9(a,b) & Q.10(a,b)

9(a)
Write a note on reinforcement learning.

8 M

9(b)
Write a note on On-line learning.

8 M

10(a)
Write a note on Deep Learning.

8 M

10(b)
Write a note on ensemble learning.

8 M

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