Essay Questions
1. (a) Define Machine Learning and explain the various types of human learning (e.g., rote learning,
learning from advice) and how they translate to ML concepts.
OR
(b) Discuss the major types of Machine Learning: Supervised, Unsupervised, Semi-supervised, and
Reinforcement Learning with practical examples for each.
2. (a) What is Data Pre-processing? Detail the steps involved in cleaning data, handling missing
values, and preparing models for supervised learning.
OR
(b) Explain the concept of Feature Engineering, including feature transformation and feature subset
selection.
3. (a) Describe the Simple Linear Regression model and explain how Maximum Likelihood
Estimation is used to determine model parameters.
OR
(b) Discuss the Support Vector Machine (SVM) algorithm. Explain how it identifies the optimal
hyperplane for classification.
4. (a) Explain the K-Means clustering algorithm. Discuss its steps and how it differs from the K-
Medoids partitioning method.
OR
(b) Compare and contrast Supervised Learning vs. Unsupervised Learning. List three real-world
applications for each.
5. (a) Describe the architecture of an Artificial Neural Network (ANN). Compare the structure and
function of a biological neuron with an artificial neuron.
OR
(b) What is Deep Learning? Provide an overview of Reinforcement Learning, focusing on the role of
agents, environments, and rewards.
Short Questions
6. List and briefly explain three real-world applications of Machine Learning.
7. What is "Feature Transformation" in data pre-processing?.
8. Briefly explain the core idea of a Decision Tree for classification.
9. What is Hierarchical Clustering? Mention its two main types.
10. Define an "Activation Function" and name two common types used in ANN.
11. What is the primary difference between a Perceptron and a Multi-layer Neural Network?.
12. Briefly define the role of "Back-propagation" in training a neural network.
1. (a) Define Machine Learning and explain the various types of human learning (e.g., rote learning,
learning from advice) and how they translate to ML concepts.
OR
(b) Discuss the major types of Machine Learning: Supervised, Unsupervised, Semi-supervised, and
Reinforcement Learning with practical examples for each.
2. (a) What is Data Pre-processing? Detail the steps involved in cleaning data, handling missing
values, and preparing models for supervised learning.
OR
(b) Explain the concept of Feature Engineering, including feature transformation and feature subset
selection.
3. (a) Describe the Simple Linear Regression model and explain how Maximum Likelihood
Estimation is used to determine model parameters.
OR
(b) Discuss the Support Vector Machine (SVM) algorithm. Explain how it identifies the optimal
hyperplane for classification.
4. (a) Explain the K-Means clustering algorithm. Discuss its steps and how it differs from the K-
Medoids partitioning method.
OR
(b) Compare and contrast Supervised Learning vs. Unsupervised Learning. List three real-world
applications for each.
5. (a) Describe the architecture of an Artificial Neural Network (ANN). Compare the structure and
function of a biological neuron with an artificial neuron.
OR
(b) What is Deep Learning? Provide an overview of Reinforcement Learning, focusing on the role of
agents, environments, and rewards.
Short Questions
6. List and briefly explain three real-world applications of Machine Learning.
7. What is "Feature Transformation" in data pre-processing?.
8. Briefly explain the core idea of a Decision Tree for classification.
9. What is Hierarchical Clustering? Mention its two main types.
10. Define an "Activation Function" and name two common types used in ANN.
11. What is the primary difference between a Perceptron and a Multi-layer Neural Network?.
12. Briefly define the role of "Back-propagation" in training a neural network.
