Complete Syllabus & Concept Hierarchy Mind Map Prompt (NotebookLM)

ROOT: Machine Learning Paradigms ├── 1. Supervised Learning (Labeled Target Y) │ ├── Regression -> Linear / Ridge (L2 prevents collinearity) │ └── Classification -> Tree Ensembles (XGBoost: Sequential residual boosting) ├── 2. Unsupervised Learning (Latent Structure) │ ├── Clustering -> K-Means (Sensitive to outliers) vs DBSCAN (Density-based) │ └── Dimensionality Reduction -> PCA (Eigenvalue variance) vs t-SNE └── [Cross-Branch Link]: L2 Regularization in Branch 1 is mathematically equivalent to a Gaussian Prior (MAP estimation) in Bayesian Branch 3.