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    Supervised Learning Notes for GATE DA

    GATE DA Supervised Learning: 9 chapters, 21 previous year questions (81% of Machine Learning), 0 practice questions and one solved question from each chapter.

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    Supervised Learning Notes for GATE DA

    GATE DA Supervised Learning: 9 chapters, 21 previous year questions (81% of Machine Learning), 0 practice questions and one solved question from each chapter.

    About Supervised Learning Notes

    Full study notes for Supervised Learning in GATE DA, organised across 9 chapters. Each chapter page explains concepts from the basics with worked examples and the formulas you need.

    Supervised Learning Weightage in GATE DA

    Supervised Learning accounts for 21 of 26 Machine Learning previous year questions in our bank (81%), about 7 per paper across 3 papers.

    Supervised Learning Chapter Matrix

    ChapterTopicsPYQsShare of unit PYQsPractice questions
    Chapter 1 — Supervised Learning00%0
    Regression, Regularization and Gradient-Based LearningRidge Regression and Regularization, Ridge Regression and Regularization, Gradient-Based Parameter Updates, Gradient-Based Parameter Updates, Linear Least-Squares Regression, Linear Least-Squares Regression419%0
    Chapter 2 — Supervised Learning00%0
    Linear Classifiers, Discriminant Analysis and Margin-Based MethodsFisher Discriminant and Distance-Based Linear Classifiers, Fisher Discriminant and Distance-Based Linear Classifiers, Linear Separability and Perceptron-Style Updates, Linear Separability and Perceptron-Style Updates, Support Vector Machines, Margins and Support Vectors, Support Vector Machines, Margins and Support Vectors629%0
    Chapter 3 — Supervised Learning00%0
    Model Selection, Cross-Validation and Performance MetricsCross-Validation and Model Selection, Cross-Validation and Model Selection, Classification Performance Metrics, Classification Performance Metrics210%0
    Dimensionality Reduction and Model FamiliesPrincipal Component Analysis and Orthogonal Components, Principal Component Analysis and Orthogonal Components, Generative, Discriminative and Dimensionality Reduction Models, Generative, Discriminative and Dimensionality Reduction Models210%0
    Decision Trees, Naive Bayes and k-Nearest Neighborsk-Nearest Neighbors Classification, k-Nearest Neighbors Classification, Naive Bayes Parameter Estimation, Naive Bayes Parameter Estimation, Decision Trees and Attribute-Based Classification, Decision Trees and Attribute-Based Classification314%0
    Neural Networks and Activation FunctionsReLU Activation Properties and Gradients, ReLU Activation Properties and Gradients, Neural Network Architecture, Parameters and Equivalence, Neural Network Architecture, Parameters and Equivalence419%0

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