Regression
Regression
Simple Regression: Linear Regression with a Single Input
Estimating the function from the data
Simple Linear Regression
Fitting the line to data: the Quality Metric
Model vs Fitted Line
Interpreting the Coefficients
Defining the Least Squares Optimization Objective
Concave and Convex functions
Hill Climbing – finding the maximum of a concave function
Hill Descent - Finding the minimum of a convex function
Choosing the step size
Choosing when to stop
Moving to multiple dimensions – Gradient Descent
Computing the Gradient of RSS
Approach 1 – Closed-form Solution
Approach 2: Gradient Descent of RSS(w)
Closed-form vs Gradient Descent
Influence of High Leverage points
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Regression
Regression
Simple Linear Regression
Regression Week 1 Notes v1
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