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Linear Regression

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning Setup Jul 29, 2026 · 1:18:10 The core of supervised learning is minimizing error using $\text{L}_2$ loss. Optimization relies on Gradient Descent, which scales to massive datasets using Stochastic Gradient Descent (SGD) and mini-batching.

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 2: Supervised Learning Setup

The core of supervised learning is minimizing error using $\text{L}_2$ loss. Optimization relies on Gradient Descent, which scales to massive datasets using Stochastic Gradient Descent (SGD) and mini-batching.

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