← GATE DA guide GATE DA · Section 6
Machine Learning
The ML methods are a dedicated subject; their maths foundations sit in Sections 1 and 2.
This section is a computer-science subject outside our mathematics range — the full syllabus is listed below so your preparation stays complete.
Full syllabus — Section 6
- Supervised learning — regression and classification; simple and multiple linear regression, ridge and logistic regression, k-nearest neighbour, naive Bayes, linear discriminant analysis, support vector machine, decision trees; bias-variance trade-off; cross-validation (LOO, k-fold); multi-layer perceptron, feed-forward neural network
- Unsupervised learning — clustering (k-means/k-medoid, hierarchical single- and multiple-linkage); dimensionality reduction; principal component analysis (PCA)
Not in our range — The machine-learning methods themselves — regression/classification models, SVM, decision trees, neural networks and clustering. Their mathematical foundations (regression and correlation in Section 1; PCA via the SVD / eigen-decomposition in Section 2) are in our books, but the ML methods are a dedicated subject.