CS223 · Annotated SlidesChapter 6 — Applications to Linear Models
Chapter 6 · Annotated slides

Applications to Linear Models

Regression as least squares: the design matrix X, parameter vector β and observation vector y, the least-squares line y = β₀ + β₁x, residuals, and fitting other curves.

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Slide 1: parameter vector observation vector
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  1. Linear model y = Xβ + ε. Least-squares line: X has a column of 1s and a column of x values; solve XᵀXβ = Xᵀy.
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Set up the design matrix for fitting y = β₀ + β₁x to points (2, 1), (5, 2), (7, 3).

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      1. Other curves (e.g. y = β₀ + β₁x + β₂x²) are still LINEAR in the parameters: add a column for x².
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