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GATE DA Exam Prep
·
Module 4
Supervised Learning Fundamentals
1
Regression vs. Classification: Linear Models
Differentiate regression and classification, and describe linear models for both (Linear/Logistic Regression).
2
Intuition Behind Non-Linear Classifiers
Describe the intuition behind key non-linear classifiers (k-NN, Naive Bayes, SVM, Decision Trees).
3
Bias-Variance Trade-off and Cross-Validation
Explain the bias-variance trade-off and the role of cross-validation in model evaluation.
4
Understanding Feed-Forward Neural Networks (MLPs)
Describe the basic structure of a feed-forward neural network (Multi-Layer Perceptron).
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Data Management & Algorithms
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Unsupervised Learning & Dimensionality Reduction