Dept. of CSE - Artificial Intelligence Machine Learning R19 Course Handout A.Y.

2022-23 Sem - 1

12. Question Bank

Q.No Unit CO PO BT Marks Question

Define Machine Learning and Discuss some applications of machine
1 1 CO 1 PO 1 L1 5
learning with examples.
2 PO 2 L1 3 What is well- posed learning problems
Describe the following problems with respect to Tasks,
3 PO 5 L3 10
Performance and Experience:
4 PO 3 L3 8 Ellaborate the steps used for Designing a Learning System

5 PO 2 L1 5 List the Perspectives and Issues in Machine Learning

6 PO 2 L1 5 What is Concept Learning Task and how to use it for search

7 PO 4 L1 7 How to find a Maximal Specific Hypothesis using Find s Algorithm

8 PO 2 L2 3 Define version space

Write the candidate elimination algorithm and illustrate with
9 PO 1 L3 8
example
10 PO 4 L2 5 Explain the General-to-Specific Ordering of Hypotheses

11 PO 2 L3 3 Ealborate inductive Bias with example

12 PO 3 L2 10 Explain Gradient Descent Algorithm and its variants in Detail

What is the Difference between supervised and unsupervised
13 2 CO 2 PO 4 L2 4
learning algorithms
Calculate the regression coefficient and obtain the lines of
14 PO 2 L3 10
regression for the following data X=1,2,3,4,5,6,7
15 PO 3 L2 5 What is the difference between Linear and Logistic Regression
Explain the Gradient Descent algorithm with respect to linear
16 PO 4 L2 8
regression.
Calculate the regression coefficient using Multiple regression and
17 PO 2 L3 10
obtain the lines of regression for the following data X1=3,4,5,6,2
18 PO 3 L2 10 Explain Logistic Regression with example?
Find that a patient with the following symptoms have the flu
19 PO 2 L3 8
Chills Runny Nose Headache Fever Flu
Calculate Conditional Probalitity where
20 PO 2 L3 8
B=> Day=”Holiday”,Discount=”Yes”,FreeDelivery=”Yes” for given
Find species for
21 PO 2 L3 10
Sepal Length Sepal Width Species
Find the Support Vector Machine Hyperplane for support vectors
22 PO 2 L3 10
S1=(2¦1) S2=(2¦(-1)) S3=(4¦0) .classify the point (x1,x2)=(4,2)

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Dept. of CSE - Artificial Intelligence Machine Learning R19 Course Handout A.Y. 2022-23 Sem - 1

Find the Non Linear Support Vector Machine Hyperplane for

23 PO 2 L3 14
support vectors (1¦1)((-1)¦1)((-1)¦(-1))(1¦(-1))(2¦0)(0¦2)((-
Construct the decision tree for given dataset using ID3.
24 PO 5 L3 14
Outlook Temperature Humidity Wind Play Tennis
Construct decision tree using CART for given dataset (Classification
25 PO 5 L3 14
and Regression Tree) and check tennis can be played for test data=”
Construct the decision tree for given dataset using
26 PO 5 L3 14
CART(Classification and Regression Tree)
27 PO 1 L1 4 What is Over Fitting and Generalization,Bias and Variance

28 PO 1 L1 2 How to Calculate Entropy and Information Gain

29 3 CO 3 PO 2 L1 4 How gain ratio is efficient than Information gain in Decision tree

write the formulas to calculate
30 PO 1 L2 5
i) Gini Index
Construct the decision tree for given dataset using C4.5
31 PO 5 L3 14
age income student Credit_ranking buys_computer
Construct decision tree using CHAID for given dataset and check
32 PO 5 L3 14
tennis can be played for test data=” Rain ,Cool, High,Weak”
A class of 9 students got marks X out of 30
33 PO 3 L3 8
X={2,3,4,10,11,12,20,25,30} ,Now cluster then into groups where
Find the clusters for given 2D dataset for K=2, K=3
34 PO 3 L1 10

35 PO 3 L2 10 Explain in Detail Gaussian Mixture Model Clustering

36 PO 4 L2 8 Differentiate between K Mean and Hierarchical Clustering.

Find the clusters using a Agglomerative Single link Technique for
37 PO 2 L3 10
given dataset and draw the dendogram
Find the clusters using a Agglomerative Single link Technique for
38 PO 2 L3 10
given distance matrix and draw the dendogram
Find the clusters using a Agglomerative Complete link Technique for
39 PO 2 L3 10
given distance matrix and draw the dendogram
Find the clusters using a Agglomerative Average link Technique for
40 PO 2 L3 10
given distance matrix and draw the dendogram
Find the clusters using a Divise Hierarchial Clustering for given
41 PO 2 L3 10
datasets and draw the dendogram
42 PO 2 L2 14 Explain about random forest algorithm in detail
Explain in detail about bagging, Bootstrappingand boosting
43 PO 2 L2 5
concepts
44 PO 2 L2 5 Explain Out of bag Cocnept with example

45 4 CO 4 PO 3 L2 8 Explain the Architecture of Single Layer Neural networks in detail

46 PO 3 L2 8 Explain the Architecture of Multi Layer Neural networks in detail

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Dept. of CSE - Artificial Intelligence Machine Learning R19 Course Handout A.Y. 2022-23 Sem - 1

47 PO 1 L2 5 Explain Activation Function in detail

Adjust the weights using self organizing maps where number of
48 PO 2 L2 14
input vectors=4,clusters=2,random weights are
49 PO 3 L2 10 Explain Backpropogartion algorithm in detail
Update weights using Backpropogation in Multilayered perceptron
50 PO 2 L3 14
network
Explain about Haars Cascade Classifier in detail used for face
51 PO 3 L2 10
recognition
52 PO 2 L1 6 Explain about perceptions in detail

53 PO 4 L1 10 Ellobarate the Error Minimization procedure in ANN

54 PO 2 L1 10 What is Reccurent network,Explain in detail

55 5 CO 5 PO 2 L1 7 What is Genetic Algoritm,Explain with example

56 PO 5 L1 8 What is a Mutation and why is it programmed into the algorithm

57 PO 3 L2 6 Explain hypotheses space search

58 PO 2 L1 10 How to parallelize genetic Algorithms

59 PO 2 L2 14 Explain reinforcement learning in detail

60 PO 2 L2 8 Explain about learning task in detail

61 PO 2 L2 10 Explain about Q learning in detail

62 PO 4 L1 6 Define Non deterministic,rewards,action

63 PO 2 L1 4 What is temporal difference Learning

64 PO 4 L1 4 Define rewards and actions

65 PO 5 L1 10 How dynamic programming is applicable in Reinforcement Learning

66 PO 2 L2 5 Difference Between Reinforcement and Supervised Learning

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