STUDY MATERIAL · GATE DA
GATE Machine Learning: questions and notes
Machine Learning questions from 3 GATE papers (2024–2026) with the official answers, each solved step by step, plus notes for each topic, a formula sheet and practice questions.
- previous-year questions
- 28previous-year questions
- solved step by step
- 28solved step by step
- GATE papers, 2024–2026
- 3GATE papers, 2024–2026
- topics with notes
- 7topics with notes
TOPICS
Seven topics, in the order they are taught
Taken from the official GATE 2027 syllabus. Each topic has notes, the formulas you need, its previous-year questions with the official answers and step-by-step solutions, and practice questions.
Simple, multiple and ridge regression
Regression predicts a real-valued target from features.
4 previous-year questions · 6 marks · 5 practice questions
Logistic regression, k-nearest neighbour and naive Bayes
Logistic regression, k-nearest neighbour and naive Bayes are the three basic classifiers in the GATE DA Machine Learning unit.
4 previous-year questions · 5 marks · 6 practice questions
Linear discriminant analysis, support vector machines and decision trees
Linear discriminant analysis, support vector machines and decision trees are three classic classifiers.
7 previous-year questions · 11 marks · 6 practice questions
Bias–variance trade-off and cross-validation
Every model is judged by how well it does on data it has not seen, not on the data it was fitted to.
2 previous-year questions · 3 marks · 7 practice questions
Multi-layer perceptrons and feed-forward neural networks
A feed-forward neural network is a stack of layers, each computing a weighted sum of the previous layer's outputs followed by a non-linear activation.
5 previous-year questions · 9 marks · 7 practice questions
Clustering: k-means, k-medoids and hierarchical clustering
Clustering groups unlabelled points so that points in a group are close and points in different groups are far apart.
4 previous-year questions · 6 marks · 5 practice questions
Dimensionality reduction and principal component analysis
Dimensionality reduction replaces many correlated features by a few new ones that keep as much of the data's variance as possible.
2 previous-year questions · 3 marks · 5 practice questions
QUESTIONS BY TOPIC AND YEAR
28 questions, by topic and year
How many questions each topic had in the DA papers of each year. This shows what was asked, not what will be asked. We placed each question under a topic ourselves; the exam paper does not label topics. Every question links back to its paper.
| Topic | 2024 | 2025 | 2026 | Total |
|---|---|---|---|---|
| Regression | · | 1 | 3 | 4 |
| Logistic, k-NN, naive Bayes | 3 | 1 | · | 4 |
| LDA, SVM & trees | 4 | 2 | 1 | 7 |
| Bias–variance & CV | · | · | 2 | 2 |
| Neural networks | 1 | 3 | 1 | 5 |
| Clustering | 2 | 1 | 1 | 4 |
| PCA | · | 1 | 1 | 2 |
All three GATE DA papers are included: 2024 (IISc Bengaluru), 2025 (IIT Roorkee) and 2026 (IIT Guwahati), each with its final answer key.
Machine Learning formulas by topic
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THE OFFICIAL SYLLABUS
What each paper lists under Machine Learning
From the GATE 2027 syllabus PDF: all of DA Section 6, “Machine Learning”.
GATE DA
Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.
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