SAL Institute of Technology & Engineering Research

CE/CSE/ICT Department

Machine Learning (3170724)

Question Bank
Year: 2024-2025

Prepared By:

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CHAPTER NO - 1 : Introduction to Machine Learning (CO-1) Marks
No.
1 Define Machine learning? Briefly explain the types of learning. (June 2022) 3
2 Define Machine learning and list out few applications in Engineering?(January 2023) 3
3 Distinguish lazy vs eager learner with an example.(January 2023) 4
Explain the concept of penalty and reward in reinforcement. Learning. (June 2022, December
4 4
2021)

What do you mean by a well-posed learning problem? Explain important features that are
5 7
required to well-define a learning problem. (June 2022, December 2021)

6 List and explain the types of machine learning in brief. (June 2022) 7
7 Define issues in machine Learning. (June 2022) 3

8 Give the difference between supervised learning and unsupervised learning. (December 2021) 3
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CHAPTER NO - 2 : Preparing to Model (CO-1) Marks
No.
9 What is categorical data? Explain its types with examples.(January 2023) 4
10 What is the purpose of Singular value decomposition? How does it achieve?(January 2023) 4
Define feature and explain the process of transforming numeric features to categorical features
11 7
with suitable example.(January 2023)
12 How can we take care of outliers in data? (June 2022, December 2021) 3
13 Draw and explain the flow diagram of machine learning procedure. (June 2022) 7

14 What are the Techniques Provided in Data Pre-processing? Explain in brief (June 2022) 7
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CHAPTER NO - 3 : Modelling and Evaluation (CO-1) Marks
No.
What is model accuracy in reference to classification? Also Explain the performance parameters
15 7
Precision, Recall and F-measure with its formula and example.(January 2023)
List the methods for Model evaluation. Explain each. How we can improve the performance of
16 7
model. (June 2022)
17 Explain the training of Predictive Model. (June 2022) 3
Consider the following confusion matrix of the win/loss prediction of cricket match. Calculate
model accuracy and error rate for the same.
18 4

(December 2021)

19 Explain K-fold cross validation method with suitable example. (January 2023, December 2021) 7
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CHAPTER NO - 4 : Basics of Feature Engineering (CO-1) Marks
No.
20 What is principal component analysis? How does it work? Explain.(January 2023) 7
21 Differentiate PCA and LDA. (June 2022) 4
22 Explain the need of feature engineering in ML. (June 2022) 3

23 Explain SVD as a feature extraction technique with suitable example. (December 2021) 7
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CHAPTER NO - 5 : Overview of Probability (CO-2) Marks
No.
24 What is conditional probability? Define its importance.(January 2023) 3
25 Explain posterior probability with its formula.(January 2023) 3
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 Define the following terms.
(i) Variance
26 3
(ii) Covariance
(iii) Joint Probability(January 2023)
27 What is Bernoulli distribution? Explain briefly with its formula.(January 2023) 3
Explain Key elements of Machine Learning. Explain various function approximation methods.
28 4
(June 2022)
29 What is likelihood probability? Give an example. (June 2022, December 2021) 3,4
30 What is data sampling? Explain data sampling methods? (June 2022) 4
31 Explain Binomial Distribution with an example. (June 2022) 4
32 Explain Monte Carlo Approximation. (June 2022) 7
If 3% of electronic units manufactured by a company are defective. Find the probability that in a
33 3
sample of 200 units, less than 2 bulbs are defective.

In a communication system each data packet consists of 1000 bits. Due to the noise, each bit may
34 be received in error with probability 0.1. It is assumed bit errors occur independently. Find the 3
probability that there are more than 120 errors in a certain data packet. (December 2021)

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CHAPTER NO - 6 : Bayesian Concept Learning (CO-2) Marks
No.
35 Explain Bayes’ theorem in details. (June 2022) 7
36 Explain the concept of Bayesian belief network.(January 2023) 4
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CHAPTER NO - 7 : Supervised Learning: Classification and Regression (CO-3) Marks
No.
37 Explain KNN algorithm with suitable example.(January 2023) 7
38 Show various distance-based similarity measure with its example.(January 2023) 4
39 Explain decision tree approach with suitable example.(January 2023) 7
40 Define Entropy. Show its importance with suitable example.(January 2023) 4
41 Define linear regression. Also explain Sum of squares with its formula.(January 2023) 4
42 What are the strengths and weaknesses of SVM?(January 2023)
43 Explain the process of Supervised Learning Model. (June 2022) 7
44 Write a note on KNN. (June 2022) 4

45 List Classification algorithms. Explain Decision Tree as classification method. (June 2022) 4

Define:
a. Supervised Learning
46 3
b. Classification
c. Regression (June 2022)

47 Explain how Naïve Bayes classifier is used for Spam Filtering. (December 2021) 4

48 Discuss appropriate problems for decision tree learning in detail. (December 2021) 7

49 Discuss the error rate and validation error in the kNN algorithm. (December 2021) 7

50 Explain sum of squares due to error in multiple linear regression with example. (December 2021) 3

Explain how the Market Basket Analysis uses the concepts of association analysis. (December
51 7
2021)
Explain dependent variable and an independent variable in a linear equation with example.
52 3
(December 2021)
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53 What are the conditions of a negative slope in linear regression? (December 2021) 3
54 What are the factors determining the effectiveness of SVM? (December 2021) 3
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CHAPTER NO - 8 : Unsupervised Learning (CO-4) Marks
No.
55 Mention few applications areas of unsupervised learning in Engineering.(January 2023) 3
How does the apriori principle help in reducing the calculation overhead for a market basket
56 7
analysis? Explain with an example.(January 2023)
57 Explain the Apriori algorithm for association rule learning with an example. (December 2021) 7
58 Briefly explain K-Medoids.(January 2023) 4
59 What is Clustering? Explain K-mean clustering algorithm. (January 2023, June 2022) 7
Describe the concept of single link and complete link in the context of hierarchical clustering.
60 4
(December 2021)
Describe the main difference in the approach of k-means and k-medoids algorithms with a neat
61 4
diagram. (December 2021)
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CHAPTER NO - 9 : Neural Network (CO-5) Marks
No.
62 Briefly explain Perceptron and Mention its limitation.(January 2023) 3
Show the Step, ReLU and sigmoid activation functions with itsequations and sketch.(January
63 7
2023)
64 What is difference between Machine Learning and Deep Learning. (June 2022) 3
65 Write a short note on feed forward neural network. (June 2022) 4
66 Explain Rosenblatt’s perceptron model. (December 2021) 4
Describe, in details, the process of adjusting the interconnection weights in a multi-layer neural
67 7
network. (December 2021)

68 Draw a flow chart which represents backpropagation algorithm. (December 2021) 4

Explain, with example, the challenge in assigning synaptic weights for the interconnection
69 7
between neurons? How can this challenge be addressed? (December 2021)

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