GATE DA Machine Learning syllabus
Supervised learning, model validation, neural networks, clustering and PCA.
About Machine Learning in GATE DA
The GATE DA 2027 syllabus lists 13 topics under Machine Learning, in 2 areas: Supervised learning and Unsupervised learning.
It is one of the 7 subjects that together carry 85 of the 100 marks (Data Science & AI subjects). General Aptitude adds 15 marks and is common to every paper. The exam has 65 questions (MCQ, MSQ and NAT) in 3 hours.
See the full GATE DA syllabus for every subject and the marks distribution.
Machine Learning topics
Supervised learning
- Regression and classification problems
- Simple linear regression, multiple linear regression and ridge regression
- Logistic regression
- k-nearest neighbour and naive Bayes classifier
- Linear discriminant analysis
- Support vector machine
- Decision trees
- Bias–variance trade-off
- Cross-validation: leave-one-out (LOO) and k-fold
- Multi-layer perceptron and feed-forward neural networks
Unsupervised learning
- Clustering algorithms: k-means and k-medoid
- Hierarchical clustering: top-down and bottom-up; single-linkage and multiple-linkage
- Dimensionality reduction: principal component analysis (PCA)
Machine Learning PYQs & weightage
SoonGATE DA previous year questions (PYQs) on Machine Learning, with answers, solutions and topic-wise weightage, will appear here as they are published.