Classical Machine Learning

This section covers foundational and advanced classical machine learning algorithms, mathematical formulations, optimization techniques, and evaluation frameworks.

Key Focus Areas

  • Supervised Learning: Linear/Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Ensemble Methods (Random Forests, Gradient Boosting).
  • Unsupervised Learning: K-Means Clustering, PCA, Dimensionality Reduction.
  • Model Evaluation & Validation: Bias-Variance tradeoff, cross-validation, evaluation metrics.
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