BANGALORE COLLEGE OF ENGINEERING & TECHNOLOGY

(Approved by AICTE, NewDelhi, Affiliated toV.T.U, Belagavi, Recognized by Govt.of.Karnataka)

Chandapura, Bengaluru-560099
Department of Computer Science & Engineering
Question Bank

SUBJECT TITLE MACHINE LEARNING

SUBJECT CODE BCS602

ACADEMIC YEAR 2024-25

SCHEME 2022 BATCH 2022-26

SEMESTER VI

FACULTY NAME

MODULE 1

QNO QUESTIONS Bloom’s COs

TL
1. What is the need for Machine Learning in today’s data-driven world? L2 CO1
2. Define Machine Learning. Explain its relationship to other fields with diagram L1 CO1
3. Explain different types of machine learning with a diagram L2 CO1
4. Discuss the major challenges faced in Machine Learning. L2 CO1
5. Illustrate the steps involved in a typical Machine Learning process. L2 CO1
6. List and explain key real-world applications of Machine Learning. L2 CO1
7. List out and briefly explain about classification algorithm. L2 CO1
8. List out and briefly explain about unsupervised algorithm. L2 CO1
9. Define data. Explain 6V’s of Big Data L1 CO1
10. Explain data preprocessing with an example L2 CO1
11. Explain in stages the data management life cycle. L2 CO1
12. Explain types of Big Data with example L2 CO1
13. Explain in detail data cleaning process. L2 CO1
14. Explain in detail univariate data analysis L2 CO1
 MODULE 2

Bloom’s COs
QNO QUESTIONS TL
1. Apply and explain principal component analysis algorithm for the given data L3 CO2

points and prove that PCA works.

2. Explain continuous and discrete probability distributions L2 CO2
3. Design a learning system for chess game L3 CO2
4. L3 CO2
5. Explain the concept of Version Spaces in Machine Learning. Apply Find S- L3 CO2
algorithm for the given instances.
Time Weather Temperature Company Humidity Wind Goes
Mornin g Sunny Warm Yes Mild Strong Yes
Evening Rainy Cold No Mild Normal No
Morning Sunny Moderate Yes Normal Normal Yes
Evening Sunny Cold Yes High Strong Yes
6. Write Candidate Elimination algorithm and apply the same for given instances L3 CO2

7. Explain in detail about Dimensionality Reduction Techniques. L2 CO2

8. L2 CO2
 9. L2 CO2

10. L2 CO2

11. L1 CO2

12. L3 CO2

13. L2 CO2

MODULE 3
QNO QUESTIONS Bloom’s COs
TL
1 Distinguish between L4 CO3
i. Locally weighted regression and Linear regression
ii. Multiple linear regression and Logistic regression
2 Analyze decision tree learning with its structure, advantages, and disadvantages. L4 CO3
3 Apply weighted KNN algorithm using the given dataset to classify the test set data L3 CO3
(7.6, 60,8) where k=3
 4 Make use of entropy and information gain to discover the root node for the L3 CO3
decision tree for the following dataset using ID3 algorithm.

5 Consider the training dataset. Construct ID3,C4.5,CART L3 CO3

6 Explain the working of the K-Nearest Neighbor (KNN) algorithm. L2 CO3

7 How is Weighted KNN different from standard KNN? L2 CO3
8 Describe the Nearest Centroid Classifier and its use cases. L2 CO3
9 What is Locally Weighted Regression? How is it useful? L2 CO3
10 Differentiate between Linear, Multiple Linear, and Polynomial Regression. L2 CO3
11 What is Logistic Regression? Discuss its application in classification. L2 CO3
12 Describe the Decision Tree Learning Model. L2 CO3
13 Explain the Decision Tree Induction Algorithm with an example. L2 CO3
14 Discuss advantages and disadvantages of Decision Trees. L2 CO3
15 What are the evaluation metrics for regression and classification models? L2 CO3
16 Explain the working of the K-Nearest Neighbor (KNN) algorithm. L2 CO3

MODULE-4
QNO QUESTIONS Bloom’s COs
TL
1 Define prior probabaility.Explain Bayes theorem, hML and hMAP with an L1 CO4
example

2 Analyze the student performance using Navie Bayes algorithm for continuous L2 CO4
attribute. Predict whether student will get job offer or not in the final year.
 3 Analyze different types of artificial neural network with diagram L4 CO4
4 Define activation function. Explain different types of activation function. L1 CO4

5 Explain Bayes' Theorem and its application in Machine Learning. L2 CO4

6 Describe the Naïve Bayes algorithm for continuous attributes. L2 CO4
7 How does Probability-based learning differ from similarity-based learning? L2 CO4
8 Discuss the classification process using Bayes models. L2 CO4
9 Describe the structure of a biological neuron and its analogy in ANN. L2 CO4
10 What is a perceptron? Explain the learning rule. L1 CO4
11 Discuss the advantages and disadvantages of using ANNs. L2 CO4

MODULE-5

QNO QUESTIONS Bloom’s COs

TL
1 Analyze Grid based approach and mention the steps of CLIQUE L4 CO5
2 Apply k means clustering algorithm for the given data with initial value of objects L3 CO5

3 Determine characteristics, application and challenges of reinforcement learning L3 CO5

4 Analyze components of reinforcement learning with a diagram L4 CO5
5 Explain K-Means Clustering algorithm with steps and an example L2 CO5
6 Explain Hierarchical Clustering algorithms. Differentiate between Agglomerative L2 CO5
and Divisive methods
7 Explain working of DBSCAN algorithm and CLIQUE algorithm. L2 CO5
8 Explain Q-Learning Algorithm with an example. Compare it with SARSA L3 CO5
9 Define Markov Decision Process(MDP).Explain its components with an example L2 CO5
10 What is Reinforcement Learning? Explain its framework with neat diagram and L2 CO5
real-world applications
11 Compare and contrast different clustering approaches. L2 CO5
12 What are proximity measures? How do they affect clustering? L2 CO5
13 Describe the Hierarchical Clustering Algorithm with steps. L3 CO5
14 Explain the K-Means algorithm and its limitations. L2 CO5
15 What are density-based clustering methods? Give examples. L2 CO5
16 Define Reinforcement Learning and explain its scope. L3 CO5
17 Discuss the components of a Reinforcement Learning system. L3 CO5
18 What is the Markov Decision Process? Explain its role in RL. L2 CO5
19 Explain the Multi-Armed Bandit Problem and its relevance. L2 CO5
20 Differentiate between Q-Learning and SARSA algorithms. L3 CO5

FACULTY HOD