Machine Learning · Question Bank

Every question from the MAKAUT papers of 2022-23 to 2025-26 (4 papers, 129 questions), sorted by topic in study order.

# Reading Guide

• Each line ends with the year(s) it was asked and the marks. Repeated questions come first in each topic, shown once with every year.
• A question that touches two topics also appears under the second one, with a link to its main topic.
• Other papers (collapsed): similar MAKAUT subjects from other branches or older schemes. Good for practice. They are not counted in the priorities.
• Notes for each topic → ML · Short on time? → Priority List

# Unit 0 · Introduction to Machine Learning

# 0.1 Introduction to Machine Learning: Definition, AI vs ML vs DL ★★

Notes → 0.1

• Machine learning algorithm build a model which is based on sample data is known as ____. 2023-24 1M
• Differentiate Between Machine Learning and Deep Learning. 2023-24 5M · main topic 4.4
• How is machine learning related to AI? 2022-23 4M
• What is Artificial Intelligence and why do we need it? 2022-23 5M
• Differentiate between Artificial intelligence, Machine Learning, and Deep Learning 2022-23 6M

# 0.2 Types of Machine Learning ★★★

Notes → 0.2

• What is unsupervised learning? 2024-25 2023-24 1M
• Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in ____learing algorithm. 2023-24 1M
• Discuss the different types of Machine Learning? 2022-23 6M

# 0.3 Data Basics: Features, Variable Types, Data Splits & Pre-processing ★

Notes → 0.3

• A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Feature F1 is an example of ____ variable. 2022-23 1M
• The ____ is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter. 2022-23 1M

# 0.4 Parametric vs Non-parametric Models ★★★

Notes → 0.4

• What are parametric and non-parametric model. 2025-26 2022-23 5M
• Why KNN is non-parametric? 2023-24 3M · main topic 1.3

# Unit 1 · Supervised Learning

# 1.1 Supervised Learning: Classification vs Regression ★★

Notes → 1.1

• Compare and contrast classification and regression tasks in supervised machine learning. Provide examples of each. 2025-26 · 4M 2022-23 · 5M

# 1.2 Distance-Based Methods & Distance Measures ★★

Notes → 1.2

• How does the choice of distance metric affect the performance of a KNN classifier? 2024-25 5M · main topic 1.3
• What is Euclidean distance in terms of machine learning? Using Euclidean distance find the distance between points P(3, 2) and Q(4, 1). 2023-24 6M

# 1.3 K-Nearest Neighbours (KNN) ★★★

Notes → 1.3

• How does the choice of distance metric affect the performance of a KNN classifier? 2024-25 5M
• Explain KNN algorithm with suitable example. 2024-25 8M
• Why KNN is non-parametric? 2023-24 3M
• Write down three Pros and Cons of KNN algorithm. 2023-24 6M
• Compare K-means and KNN Algorithms. 2022-23 5M · main topic 2.2
• How do we decide the value of "K" in KNN algorithm? Why is the odd value of “K” preferable in KNN algorithm? 2022-23 5M

# 1.4 Decision Trees: ID3, Entropy & Information Gain ★★

Notes → 1.4

• What is the benefit of Random forest classifier over individual decision trees. 2025-26 5M · main topic 3.9
• In a Decision Tree Leaf Node represents____. 2023-24 1M
• Explain the ID3 algorithm for decision tree learning. 2023-24 · 4M 2023-24 · 5M 2023-24 · 6M

# 1.5 Probability Basics & Bayes Theorem ★

Notes → 1.5

• Define conditional probability. Discuss Bayes theorem with suitable example. 2024-25 5M
• The Bayes rule can be used in answering ____. 2023-24 1M

# 1.7 Naive Bayes Classifier ★★

Notes → 1.7

• Why it is called Naïve Bayes ? 2024-25 · 3M 2023-24 · 5M
• Using these probabilities estimate the probability values for the new instance (Color=Green,legs =2,Height=Tall,and Smelly=No). [data below] 2024-25 · 2023-24 · 6M
• Discuss Naïve Bayes Classifier algorithm. 2024-25 4M

# 1.8 Linear Regression ★★★

Notes → 1.8

• Describe linear regression as a supervised learning algorithm. How is the best-fit line determined and how are predictions made with it? 2025-26 5M
• Compare linear regression with logistic regression. Write two applications of logistic regression. 2024-25 5M · main topic 1.9
• Discuss cost function in regression. 2024-25 5M
• Write one common feature about neural network and linear regression models. 2023-24 1M · main topic 4.3
• The selling price of a house depends on many factors. For example, it depends on the number of bedrooms, number of kitchen, number of bathrooms, the year the house was built, and the square footage of the lot. Given these factors, predicting the selling price of the house is an example of which type of linear regression. 2022-23 1M

# 1.9 Logistic Regression ★★★

Notes → 1.9

• Logistic regression is generally used for which type of classification? 2025-26 2022-23 1M
• Compare linear regression with logistic regression. Write two applications of logistic regression. 2024-25 5M

# 1.11 Support Vector Machines: Hyperplane, Margin & Support Vectors ★

Notes → 1.11

• What are support vectors in the context of SVMs? 2024-25 1M
• Define-Support Vectors, Margins and Hyperplane. 2024-25 6M
• What is the difference between a linear and non-linear SVM? 2024-25 4M · main topic 1.12

# 1.12 Non-linearity & Kernel Methods (Non-linear SVM) ★

Notes → 1.12

• What is the difference between a linear and non-linear SVM? 2024-25 4M

# Unit 2 · Unsupervised Learning

# 2.1 Clustering: Concepts, Requirements & Types ★

Notes → 2.1

• What do you mean by Clustering? 2024-25 1M
• State the requirement of clustering algorithm? 2024-25 5M
• How does the clustering differ from classification in machine learning? 2024-25 5M
• True or False: Hierarchical clustering is slower than non-hierarchical clustering? 2022-23 1M
• Targeted marketing, Recommended Systems, and Customer Segmentation are applications in which algorithm? 2022-23 1M

# 2.2 K-means Clustering ★★★

Notes → 2.2

• Describe the K-means clustering algorithm. 2025-26 5M
• Describe the strengths and weaknesses of the K-Means clustering algorithm. When is it suitable for use? 2024-25 5M
• True or False: K-means clustering aims to partition n observations into k clusters. 2023-24 1M
• Compare K-means and KNN Algorithms. 2022-23 5M

# 2.4 Dimensionality Reduction & the Curse of Dimensionality ★★★

Notes → 2.4

• Discuss problems associated with high-dimensional data, highlighting the concept of the "curse of dimensionality". 2025-26 2022-23 5M
• Define dimensionality reduction and explain its significance in data processing. 2025-26 5M
• Give one example of Dimensionality reduction technique. 2024-25 1M

# 2.5 Feature Selection ★★

Notes → 2.5

• Explain the importance of feature selection in supervised machine learning. What techniques can be used for feature selection and what factors should be considered when choosing features? 2025-26 · 6M 2022-23 · 6M 2022-23 · 4M

# 2.6 Principal Component Analysis (PCA) ★★★

Notes → 2.6

• What does PCA stands for? 2025-26 2024-25 1M
• Explain the relationship between the variance of the data and the principal components derived using PCA. Discuss a limitation of PCA when it comes to capturing non-linear relationships in the data. 2025-26 5M
• Write down the steps of PCA (Principal Component Analysis) 2022-23 6M

# 2.7 Kernel PCA ★

Notes → 2.7

• Explain the relationship between the variance of the data and the principal components derived using PCA. Discuss a limitation of PCA when it comes to capturing non-linear relationships in the data. 2025-26 5M · main topic 2.6

# 2.8 Recommender Systems: Content-Based & Collaborative Filtering ★★

Notes → 2.8

• Write Short note on Content Based filtering and Collaborative filtering. 2024-25 6M
• How does the system make recommendations. 2024-25 4M
• Discuss mathematics behind the recommendations made using content-based filtering. 2024-25 5M

# 2.9 Matrix Factorization & Matrix Completion ★★

Notes → 2.9

• Matrix factorization plays a significant role in collaborative filtering-based recommendation systems. Explain how matrix factorization works in the context of recommendation systems. 2025-26 · 7M 2022-23 · 5M
• Discuss the challenges of matrix completion when dealing with sparse user-item interaction data. 2025-26 5M
• Describe one matrix factorization technique used for recommendation. 2025-26 3M

# 2.10 Generative Models: Mixture Models (GMM & EM) ★★

Notes → 2.10

• Explain Generative Mixture model. 2025-26 · 5M 2022-23 · 4M
• With a proper diagram explain the steps of a generative mixture model. 2025-26 2022-23 5M

# Unit 3 · Model Evaluation and Selection

# 3.1 Confusion Matrix & Classification Metrics ★★★

Notes → 3.1

• What Is a False Positive and False Negative and How are they significant with an example? 2023-24 2022-23 5M
• Why do you need Confusion matrix? 2023-24 · 4M 2022-23 · 5M
• Explain Precision, Recall and F1 Score. 2024-25 5M
• Calculate the accuracy percentage for the given Confusion Matrix. [data below] 2022-23 5M

# 3.3 Overfitting, Underfitting & the Bias–Variance Trade-off ★★★

Notes → 3.3

• Explain the concept of overfitting in machine learning. How can techniques like cross-validation help in mitigating it? 2025-26 2023-24 5M
• In deep learning, what is the role of dropout regularization? 2025-26 1M · main topic 4.4
• Write down the difference between under fitting and over fitting. 2024-25 1M
• State the Difference Between Bias and variance in ML. 2024-25 5M
• True or False: Overfitting is more likely when you have huge amount of data to train. 2023-24 1M

# 3.4 Model Selection & Cross-Validation ★★

Notes → 3.4

• What is the use of cross validation. 2025-26 · 1M 2023-24 · 5M
• Explain the concept of overfitting in machine learning. How can techniques like cross-validation help in mitigating it? 2025-26 5M · main topic 3.3
• What is model selection? Write down the types of data and based on that which model is used? 2023-24 5M

# 3.5 Introduction to Statistical Learning Theory (PAC, VC Dimension) ★

Notes → 3.5

• Explain Statistical Learning theory. 2023-24 5M

# 3.6 Ensemble Learning: Principle & Types ★★★

Notes → 3.6

• What is an ensemble model in machine learning? 2024-25 2022-23 1M
• If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance? 2023-24 1M
• True or False: Ensemble learning can only be applied to supervised learning methods. 2022-23 1M
• In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? 2022-23 1M
• Why ensemble learning is used? What is the general principle of an ensemble method and what is bagging and boosting in ensemble method? 2022-23 5M

# 3.7 Bagging & Bootstrapping ★★★

Notes → 3.7

• Define what bagging is and explain its core principle. Describe how it reduces overfitting and variance in machine learning models. 2025-26 5M
• List two benefits and one potential drawback of using bagging in the context of decision trees. 2025-26 5M
• Discuss the concept of bootstrapping in bagging and explain how it contributes to model diversity. 2025-26 5M
• Which ensemble model helps in reducing variance? 2023-24 1M
• Why ensemble learning is used? What is the general principle of an ensemble method and what is bagging and boosting in ensemble method? 2022-23 5M · main topic 3.6

# 3.8 Boosting & Bagging vs Boosting ★★★

Notes → 3.8

• Which ensemble method involves training multiple models in such a way that each model corrects the errors of the previous model? 2025-26 1M
• In ensemble methods, what is the main advantage of boosting over bagging? 2025-26 1M
• Compare between Bagging & Boosting technique. 2024-25 6M
• Why ensemble learning is used? What is the general principle of an ensemble method and what is bagging and boosting in ensemble method? 2022-23 5M · main topic 3.6

# 3.9 Random Forest ★★

Notes → 3.9

• What is the benefit of Random forest classifier over individual decision trees. 2025-26 5M
• --------machine learning algorithm is based upon the idea of bagging. 2024-25 1M
• Write short note on Random Forest algorithm. 2024-25 4M

# Unit 4 · Sparse Modeling, Sequences and Deep Learning

# 4.1 Sparse Modeling & Estimation ★★

Notes → 4.1

• What is the primary goal of sparse modeling and how does it differ from traditional modeling approaches? 2025-26 2023-24 5M
• How does L1 regularization encourage sparsity in model coefficients? 2025-26 1M

# 4.2 Modeling Sequence / Time-Series Data ★

Notes → 4.2

• Explain: Time series data in machine learning 2023-24 5M

# 4.3 Neural Network Fundamentals: Perceptron, Activation Functions & Backpropagation ★★★

Notes → 4.3

• Discuss working of Nueral network. State two differences between Deep learning and Machine learning. 2024-25 5M
• Write one common feature about neural network and linear regression models. 2023-24 1M
• The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are 2022-23 1M

# 4.4 Deep Learning: Concepts, ML vs DL & Applications ★★★

Notes → 4.4

• Define deep learning. 2024-25 · 1M 2024-25 · 1M 2022-23 · 4M
• Discuss working of Nueral network. State two differences between Deep learning and Machine learning. 2024-25 2023-24 5M · main topic 4.3
• In deep learning, what is the role of dropout regularization? 2025-26 1M
• Deep learning algorithms are ____ more accurate than machine learning algorithm in image classification. 2023-24 1M
• Differentiate between Artificial intelligence, Machine Learning, and Deep Learning 2022-23 6M · main topic 0.1

# 4.6 Feature Representation Learning ★

Notes → 4.6

• Explain how deep learning and feature representation learning related. 2023-24 5M

# Unit 5 · Scalable and Advanced ML

# 5.1 Scalable ML: Online & Distributed Learning ★

Notes → 5.1

• How does distributed learning allow for scalability in training large datasets? 2025-26 1M
• What is continual learning in ML, and why is it important? 2025-26 1M

# 5.2 Semi-supervised Learning ★

Notes → 5.2

• What is semisupervised learning in machine learning? 2024-25 2022-23 1M

# 5.3 Active Learning ★★

Notes → 5.3

• Explain Pool-Based sampling in Active learning. 2023-24 5M

# 5.4 Reinforcement Learning ★★★

Notes → 5.4

• Give an practical example of reinforcement learning. 2025-26 2023-24 2022-23 1M
• What is the main objective of an agent in reinforcement learning? 2025-26 1M
• In reinforcement learning, what term is used for the signal that indicates the quality of an action taken? 2025-26 1M
• Define Reinforcement learning. 2024-25 1M
• Explain How a system can play a Game of Chess using Reinforcement Learning. 2023-24 5M

# 5.5 Inference in Graphical Models: Bayesian Networks ★

Notes → 5.5

• Define a Bayesian network and explain how it can be used to represent probabilistic relationships between a set of variables. 2025-26 5M