multivariable linear reg

1. Multivariate Linear Regression

2. More notation

3. Hypothesis
• hθ(x) = θ0 + θ1x
• hθ(x) = θ0 + θ1x1 + θ2x2 + θ3x3 + θ4x4
• hθ(x) = θ0x0 + θ1x1 + θ2x2 + θ3x3 + θ4x4

4. Gradient Descent

5. Gradients

6. Gradient Descent in
practice: Feature
scaling
• x1 = size (0 - 2000 feet)
• x2 = number of bedrooms
(1-5)
• Divide to max
• Mean norm

7. Learning Rate α
Make sure gradient descent is working

8. Features and
polynomial regression
• Often, by defining new
features you may get a
better model

9. Normal equation
• Normal equation
solves θ analytically