SPPU Information Technology (Semester 7)
Machine Learning
December 2016
Total marks: --
Total time: --
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

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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