Dragon Notes

UNDER CONSTRUCTION
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Machine Learning:Definitions

Terminology

Hypothesis function: a learned function aiming to map inputs to outputs with least error
Mean squared error: square of difference between predicted and true outputs, $$(\hat{y}_i-y_i)^2$$.
- a measure of accuracy of hypothesis function
Decision boundary - a line (/ bounding manifold) separating the regions of positive and negative classification ($$y=1$$ and $$y=0$$)
Symbols / Notation

$$\underset{\theta_0, \theta_1}{\t{min}}\ J(\theta_0,\theta_1)$$ -- minimize cost function $$J(\theta_0,\theta_1)$$ over parameters $$\theta_0,\theta_1$$
$$:=$$ -- assignment operator; $$a := b$$ -- assign $$b$$ to $$a$$